WO2021036550A1 - 基于视觉任务的点云数据压缩质量评价方法及系统 - Google Patents

基于视觉任务的点云数据压缩质量评价方法及系统 Download PDF

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WO2021036550A1
WO2021036550A1 PCT/CN2020/101786 CN2020101786W WO2021036550A1 WO 2021036550 A1 WO2021036550 A1 WO 2021036550A1 CN 2020101786 W CN2020101786 W CN 2020101786W WO 2021036550 A1 WO2021036550 A1 WO 2021036550A1
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point cloud
cloud data
visual
task
evaluation
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French (fr)
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徐异凌
高粼遥
朱文婕
管云峰
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上海交通大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

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  • the invention relates to the field of point cloud data processing, in particular to a point cloud data compression quality evaluation method and system based on vision tasks, and a point cloud system.
  • the point cloud data obtained by measuring 3D models is becoming more accurate and huge.
  • the point cloud data is composed of three-dimensional coordinate information, texture information, depth information, etc. of the object after three-dimensional scanning.
  • general laser scanning equipment can easily obtain hundreds of thousands or even millions of point cloud data from the surface of an object. Massive point cloud data has brought a great burden to computer storage, processing and transmission.
  • Point-based computer graphics with point cloud models as the research object has received widespread attention.
  • the digital geometry processing technology of point cloud models has gradually become a research hotspot in graphics. It is used in reverse engineering, industrial manufacturing, cultural relics protection, and medicine. Visualization and other fields are widely used.
  • the 3D point cloud model can express the shape of the object more accurately, but its data volume is huge. Therefore, when storing and transmitting the 3D geometric model, it is usually necessary to compress the 3D point cloud model, such as lossy compression.
  • the evaluation methods for 3D point cloud model processing mainly include subjective evaluation methods and objective evaluation methods.
  • the subjective method is simple and intuitive, but it takes a lot of time and manpower, and is greatly affected by the observer's personal factors, which greatly reduces the practicability and accuracy of the subjective evaluation.
  • Objective evaluation methods get rid of the limitation of relying on human subjective judgments and effectively improve the efficiency of evaluation. It is the current research focus of the quality evaluation of 3D point cloud data.
  • the purpose of the present invention is to provide a point cloud data compression quality evaluation method and system based on vision tasks, and a point cloud system.
  • a method for evaluating the quality of point cloud data compression based on vision tasks includes the following steps:
  • the processing of the three-dimensional point cloud data refers to performing calculations according to a visual task processing algorithm.
  • the original three-dimensional point cloud data includes any one of the following:
  • the visual task includes any one of the following:
  • Visual subtasks such as classification, segmentation, recognition, detection, and tracking.
  • the encoding and decoding processing on the original three-dimensional point cloud data is performed by a point cloud codec, and the point cloud codec includes any one of the following:
  • MPEG G-PCC TMC13 MPEG V-PCC TMC2, Draco, etc.
  • the evaluation index refers to a statistical index used to evaluate and compare the quality of the visual task model and its effect, such as accuracy and precision.
  • the method further includes: comprehensive evaluation of multiple visual tasks: comprehensive evaluation of the point cloud data compression quality based on the evaluation results of each visual subtask and the importance of each visual task.
  • it further includes: when the visual task includes at least two visual subtasks, weights are set for each different visual subtask according to the importance of the visual subtask in a specific scene, and the evaluation results of each visual subtask are combined to obtain Result of multi-task evaluation.
  • the vision subtask adopts any one of the following evaluation indicators: accuracy rate, error rate, average accuracy, recall rate, accuracy rate, and cross-to-parallel ratio.
  • a point cloud data compression quality evaluation system based on vision tasks including:
  • Point cloud data encoding and decoding module encode and decode the input original 3D point cloud data, and output the encoded and decoded point cloud data;
  • Visual task processing module The three-dimensional point cloud data that has not been coded and decoded are processed according to the visual task processing algorithm to obtain the processed data;
  • Vision task model evaluation module According to the processed data, calculate the evaluation index of the vision task;
  • Evaluation result comparison module Compare the evaluation indicators of point cloud data before and after encoding and decoding, and evaluate the compression quality of point cloud data based on the comparison results.
  • the processing of the three-dimensional point cloud data refers to performing calculations according to a visual task processing algorithm.
  • the original three-dimensional point cloud data includes any one of the following:
  • the visual task includes any one or a free combination of at least two of the following:
  • Visual subtasks such as classification, segmentation, recognition, detection, and tracking.
  • the encoding and decoding processing on the original three-dimensional point cloud data is performed by a point cloud codec, and the point cloud codec includes any one of the following:
  • MPEG G-PCC TMC13 MPEG V-PCC TMC2, Draco, etc.
  • the evaluation index refers to a statistical index used to evaluate and compare the quality of the visual task model and its effect, such as accuracy and precision.
  • it also includes:
  • it further includes: when the visual task includes at least two visual subtasks, weights are set for each different visual subtask according to the importance of the visual subtask in a specific scene, and the evaluation results of each visual subtask are combined to obtain Result of multi-task evaluation.
  • the vision subtask adopts any one of the following evaluation indicators: accuracy rate, error rate, average accuracy, recall rate, accuracy rate, and cross-to-parallel ratio.
  • the present invention also provides a point cloud system, including: an original three-dimensional point cloud data input module: input original three-dimensional point cloud data; point cloud data encoding and decoding module: encoding and decoding the input original three-dimensional point cloud data, And output the coded and decoded three-dimensional point cloud data; and any one of the above-mentioned vision task-based point cloud data compression quality evaluation system, which includes a vision task processing module: separate three-dimensional point cloud data without and after coding and decoding According to the visual task processing algorithm, the processed data is obtained; the visual task model evaluation module: calculates the evaluation index of the vision task according to the processed data; the evaluation result comparison module: compares the evaluation index of the point cloud data before and after encoding and decoding , Based on the comparison results, evaluate the point cloud data compression quality.
  • an original three-dimensional point cloud data input module input original three-dimensional point cloud data
  • point cloud data encoding and decoding module encoding and decoding the input original three-dimensional point cloud data, And output the coded and
  • the present invention has the following beneficial effects:
  • the present invention proposes a point cloud data compression quality evaluation method and system based on vision tasks. That is, for specific visual subtasks (such as point cloud classification, segmentation, detection, recognition, etc.), consider the point cloud compression quality evaluation method for different application scenarios or purposes, and calculate based on the point cloud data before and after the codec Specific visual task evaluation index results, guide the point cloud compression quality evaluation results.
  • specific visual subtasks such as point cloud classification, segmentation, detection, recognition, etc.
  • the present invention is pertinent and can measure the point cloud compression quality according to a specific visual task.
  • the quality of point cloud compression can be comprehensively measured according to a variety of visual tasks.
  • FIG. 1 is a schematic flowchart of a method for evaluating the quality of point cloud data compression based on vision tasks in an embodiment of the present invention
  • Fig. 2 is a functional block diagram of a point cloud system in an embodiment of the present invention.
  • a method for evaluating the quality of point cloud data compression based on vision tasks includes the following steps:
  • the evaluation results for comparison include evaluation scores, evaluation division grades and other methods used for the evaluation results, and are not limited by the specific evaluation form.
  • the evaluation scores are used in this embodiment.
  • the original three-dimensional point cloud data includes any one of the following:
  • the specific visual task includes any one or a free combination of at least two of the following:
  • Point cloud classification segmentation, recognition, detection, tracking and other visual subtasks.
  • the processing algorithm of the specific vision task is:
  • the codec includes any one of the following:
  • MPEG G-PCC TMC13 MPEG V-PCC TMC2, Draco, etc.
  • it also includes:
  • the point cloud compression quality is evaluated based on a variety of visual subtasks.
  • the above steps can be performed according to the types of visual subtasks, and the comparison results of each task evaluation index and the importance of each visual subtask are integrated to evaluate the point cloud data compression quality .
  • the point cloud data compression quality evaluation system based on the vision task provided by the present invention can be implemented by the step process of the point cloud data compression quality evaluation method based on the vision task provided by the present invention.
  • Those skilled in the art can understand the point cloud data compression quality evaluation method based on the vision task as a preferred example of the point cloud data compression quality evaluation system based on the vision task.
  • the point cloud system includes a point cloud data compression quality evaluation system based on vision tasks, and the point cloud system includes:
  • Original 3D point cloud data input module Input original 3D point cloud data
  • Point cloud data encoding and decoding module perform encoding and decoding operations on the input original 3D point cloud data, and output the decoded point cloud data;
  • Vision task processing module Process the input 3D point cloud data according to the processing algorithm of the vision task, and obtain the processed data;
  • Vision task model evaluation module According to the data obtained after processing by the vision task algorithm, one or more evaluation indicators of the vision task are calculated.
  • Evaluation result comparison module Compare the evaluation indexes of the point cloud data before and after encoding after being processed by the vision task algorithm, and evaluate the point cloud data compression quality based on the comparison results.
  • it also includes:
  • the original three-dimensional point cloud data includes any one of the following: sparse point cloud and dense point cloud.
  • it also includes:
  • the vision task includes any one of the following: point cloud classification, segmentation, recognition, detection, tracking and other vision tasks.
  • it also includes:
  • the processing algorithm of the vision task is: a certain algorithm for realizing the point cloud vision task.
  • it also includes:
  • the codec includes any one of the following: MPEG G-PCC TMC13, MPEG V-PCC TMC2, Draco, etc.
  • the purpose of the present invention is to provide a task-based point cloud data compression quality evaluation method and system, which can guide the point cloud compression quality evaluation result according to a specific visual task and its evaluation index. That is, for specific visual tasks (such as point cloud classification, segmentation, recognition, etc.), consider the point cloud compression quality evaluation method of the point cloud in different application scenarios or purposes, and calculate the specific visual task based on the point cloud data before and after the codec
  • the evaluation index result guides the point cloud compression quality evaluation result.
  • the point cloud data collected by lidar is very large. Massive point cloud data is not conducive to the next transmission and storage of the computer, and it also sets up obstacles for the development of subsequent work. According to the point cloud data type classification, the point cloud data collected by lidar in autonomous driving belongs to sparse point cloud data. Therefore, sufficient compression processing point cloud data becomes a necessary processing step and highlights its important position.
  • the point cloud quality evaluation method based on the visual task has practical value for the point cloud coding of the visual task scene.
  • the specific flow chart of this method is briefly described as follows: After obtaining the original sparse point cloud data, first, use the three-dimensional point cloud target detection algorithm to process the input original sparse point cloud data, and get processed After the output data. Then, the evaluation index of the three-dimensional point cloud target detection algorithm is calculated according to the output data.
  • Commonly used 3D point cloud target detection algorithm indicators include: accuracy, recall, average accuracy, intersection and ratio, etc.
  • an evaluation index of average accuracy is taken as an example, and the average accuracy is recorded as AP.
  • the average accuracy before processing AP 1 is calculated.
  • MPEG G-PCC TMC13 a codec suitable for processing sparse point clouds, is used to encode and decode the original sparse point cloud data.
  • the same three-dimensional point cloud target detection algorithm is used to process the decoded point cloud data, and the processed output data is obtained. And calculate the evaluation index of the three-dimensional point cloud target detection algorithm according to the output data, and get the average accuracy AP 2 after processing.
  • the comparison method can adopt the ratio method or other comparison methods.
  • the ratio method is used to obtain the point cloud quality evaluation index of N types of vision tasks, which is recorded as the vision subtask evaluation result M i , where i is the vision task number, and each The used evaluation index T, the evaluation index T 1 indicates the index score value corresponding to the evaluation result before the treatment, and the evaluation index T 2 indicates the index score value corresponding to the evaluation result after the treatment.
  • the visual subtask evaluation result M i is assigned weights according to the importance of each visual subtask, which is recorded as the weight coefficient ⁇ i , and the sum of the weights of each visual task is 1, that is
  • the multi-task evaluation result that is, the comprehensive evaluation index M of the multi-vision task can be expressed as
  • the larger M the better the quality of the point cloud.
  • the point cloud quality evaluation indexes of the two vision tasks of target detection and target tracking they are respectively recorded as target detection evaluation index M 1 and target tracking evaluation index M 2 .
  • the weights are assigned according to the importance of each visual task, and the weight of each visual task is 1.
  • the target detection task weight is set to the first weight coefficient ⁇ 1
  • the target tracking task weight is set to the second weight coefficient ⁇ 2
  • ⁇ 1 + ⁇ 2 1.
  • T is used as the reference letter of the evaluation index.
  • the evaluation index T used for each vision subtask is selected They may be the same or different, or they may be partly the same. According to the actual situation, freely match the appropriate evaluation index for the visual subtask.
  • the evaluation index T selects one of the following indexes from the different vision subtasks to evaluate the result of the vision subtask: accuracy rate AC (ACcuracy), error rate ER (Error Rate) ), average precision AP (Average Precision), average precision average mAP (mean Average Precision), recall rate RC (ReCall), precision rate PR (PRecision), and IoU (Intersection over Union).
  • accuracy rate AC ACcuracy
  • error rate ER Error Rate
  • average precision AP Average Precision
  • average precision average mAP mean Average Precision
  • recall rate RC ReCall
  • precision rate PR PRecision
  • IoU Intersection over Union
  • this embodiment also provides a point cloud system.
  • the point cloud system includes a point cloud data compression quality evaluation system based on vision tasks.
  • the point cloud system includes: an original three-dimensional point cloud data input module, and point cloud data. Codec module, visual task processing module, visual task model evaluation module and evaluation result comparison module.
  • Original 3D point cloud data input module used to input original 3D point cloud data
  • Point cloud data encoding and decoding module used to encode and decode the input original 3D point cloud data, and output the decoded point cloud data
  • the visual task processing module is used to process the input three-dimensional point cloud data according to the processing algorithm of the visual task, and obtain the processed data;
  • the visual task model evaluation module is used to calculate one or more evaluation indicators of the visual task according to the data obtained after the processing of the visual task algorithm.
  • the evaluation result comparison module is used to compare the evaluation indexes of the point cloud data before and after encoding after being processed by the vision task algorithm, and evaluate the point cloud data compression quality based on the comparison results.
  • the point cloud system and the point cloud data compression quality evaluation system based on the vision task in which the vision task processing module, the vision task model evaluation module, and the evaluation result comparison module in Figure 2 are the same physical components for unprocessed and processed respectively
  • the subsequent point cloud data are implemented separately.
  • two sets of the same physical components are respectively used for functional realization, which also falls within the protection scope of the present invention.
  • the point cloud data compression quality evaluation method and system based on the vision task of the present invention, as well as the point cloud system, can consider the point cloud compression quality evaluation method under different application scenarios or purposes for specific visual subtasks. And according to the point cloud data before and after encoding and decoding, the results of specific visual task evaluation indicators are calculated to guide the evaluation results of point cloud compression quality.
  • the present invention has pertinence and can measure the quality of point cloud compression according to a specific visual task.
  • point cloud data types visual tasks, visual task processing algorithms, and codecs
  • codecs codecs
  • the following set of optional solutions can be used as an example to achieve:
  • Point cloud data types According to the current mainstream point cloud data classification types, point cloud data can be divided into sparse point cloud data and dense point cloud data. For different scenarios and needs, you can select the corresponding types of data for further processing.
  • Visual tasks point cloud classification, segmentation, recognition, detection, tracking and other visual tasks. For different scenarios and needs, one or more visual subtasks can be specified.
  • Visual task processing algorithm According to different visual subtasks, the corresponding processing algorithm can be adopted. For example, for 3D point cloud target recognition tasks, algorithms such as Second/PointRCNN/PointPillars are optional.
  • Codec According to the needs of the scene and the type of point cloud data, a suitable codec can be selected. For sparse point clouds, MPEG G-PCC TMC13/Draco etc. are optional codecs. For dense point clouds, MPEG V-PCC TMC2 etc. are optional codecs.
  • the method for evaluating the quality of point cloud data compression based on visual tasks in the present invention includes solutions that can freely combine different types of original three-dimensional point cloud data, different visual subtasks, different encoders, and different task indicators.
  • the point cloud data types include: dense point cloud and sparse point cloud.
  • Vision tasks include: classifying, segmenting, detecting, identifying, tracking, or processing other vision tasks for point cloud targets.
  • Visual task performance evaluation indicators include: accuracy rate, error rate, average precision, recall rate, precision rate, cross-to-parallel ratio, average precision average.
  • a single visual subtask is evaluated by selecting one of the evaluation indicators.
  • the dense point cloud is processed by the MPEG G-PCC TMC13 codec, and the visual subtask based on target classification is used for evaluation.
  • choose one of the visual task performance evaluation indicators for evaluation that is, select the accuracy rate from the accuracy rate, error rate, and average accuracy, for example.
  • the Draco codec is used to process the sparse point cloud, including two visual subtasks of target detection and target recognition, then one is selected for each visual subtask
  • the visual task performance evaluation index for evaluation here is different, that is, select the intersection and union ratio from the intersection ratio, average accuracy average, etc. to evaluate the target detection, and the average accuracy average to evaluate the target recognition.
  • the same evaluation index is selected for each visual subtask for evaluation.
  • the same selection is selected, that is, the intersection ratio and average accuracy mean value are selected to evaluate the target detection and target recognition.
  • the present invention can be optimized according to specific actual scenes, and is not limited by the limited combinations in Table 1.
  • a suitable point cloud codec is adopted, based on single or multiple visual sub-systems.
  • the tasks are processed separately, and the evaluation index T used to evaluate each visual subtask may be the same, different, or partially the same, and the comparative evaluation results before and after the processing are obtained.
  • the evaluation results are measured by the indicators and scope including: two visual tasks of the same type obtained after being encoded by an encoder and processed by the uncompressed point cloud data by the visual task algorithm.
  • the ratio of performance evaluation indicators for example, formula (1) can be known) or other comparison methods.
  • the point cloud quality evaluation index based on the vision task is obtained after being encoded by an encoder and the uncompressed point cloud data is processed by the vision task algorithm.
  • the ratio range is between 0-1, and the larger the value, the better the quality of the corresponding point cloud in the visual task scene.
  • the invention only uses the above method as an example to illustrate the point cloud data compression quality evaluation method and system based on the vision task, and the point cloud system is not limited to the above point cloud data types, vision tasks, and vision tasks. Selection of processing algorithms and codecs.
  • Each functional module in the point cloud data compression quality evaluation system based on vision tasks provided in this implementation corresponds to the point cloud data compression quality evaluation method based on vision tasks in the above-mentioned embodiment. Then the structure and The technical elements can be formed by the corresponding conversion of the generation method, and the description is omitted here and will not be repeated.

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Abstract

本发明提供了一种基于视觉任务的点云数据压缩质量评价方法及系统，其特征在于针对特定视觉子任务，包含点云分类、分割、检测、识别，考虑点云在不同应用场景或目的下的点云压缩质量评价方法，并根据编解码前后的点云数据计算得到的特定视觉任务评价指标结果，指导点云压缩质量评价结果，能够针对性根据特定视觉任务衡量点云压缩质量，此外还可以根据多种视觉任务综合衡量点云压缩质量。

Description

基于视觉任务的点云数据压缩质量评价方法及系统 技术领域
本发明涉及点云数据处理领域，尤其涉及一种基于视觉任务的点云数据压缩质量评价方法及系统以及点云系统。
背景技术
由于三维扫描技术和采集设备的快速发展，通过测量三维模型得到的点云数据日益精确、庞大。点云数据由物体经过三维扫描后的三维坐标信息、纹理信息、深度信息等组成。目前，一般的激光扫面设备可以从物体表面轻易获取数十万、甚至数百万点云数据。海量点云数据为计算机存储、处理和传输带来了极大的负担。
随着网格模型处理复杂度的剧增，点云模型的优势更加明显。以点云模型为研究对象的基于点的计算机图形学已经受到广泛的关注，点云模型的数字几何处理技术也逐渐成为图形学中的一个研究热点，在逆向工程、工业制造、文物保护以及医学可视化等领域得到广泛应用。
三维点云模型能更精确地表达物体的形状，但其数据量庞大。因此，在存储、传输三维几何模型时，通常需要对三维点云模型进行压缩处理，如有损压缩。目前，针对三维点云模型处理的评价方法主要有主观评价方法和客观评价方法。主观方法简单而直观，但需要耗费大量的时间和人力，并且受观测者个人因素的影响较大，大大降低了主观评价的实用性和准确性。客观评价方法摆脱了依赖人的主观判断的局限，有效提高评价的效率，是目前三维点云数据质量评价的研究重点。
但由于目前点云模型的研究尚处于起步阶段，关于点云模型的质量评价研究并不多见。此外，现有点云质量评价方法绝大多数为根据点云呈现的视觉效果对点云质量进行评价，并未根据特定视觉任务(如点云分类、分割、识别等)及特定视觉任务评价指标指导点云压缩质量评价结果。因此，如何基于特定视觉任务进行点云压缩质量评价是目前亟待解决的关键问题。
发明内容
针对现有技术中的缺陷，本发明的目的是提供一种基于视觉任务的点云数据压缩质量评价方法及系统以及点云系统。
根据本发明提供的一种基于视觉任务的点云数据压缩质量评价方法，包括以下步骤：
S1、输入原始三维点云数据；
S2、对所述原始三维点云数据进行处理，根据处理后的第一数据，计算视觉任务的第一评价指标；
S3、对所述原始三维点云数据进行编解码，将编解码后的三维点云数据进行处理，根据处理后的第二数据即处理后的解码数据，计算所述视觉任务的第二评价指标；
S4、对比第一评价指标与第二评价指标得出评价结果，依据该评价结果对点云数据压缩质量进行评价。
优选地，所述对三维点云数据进行处理指根据视觉任务处理算法进行运算。
优选地，所述原始三维点云数据包括以下任一种：
稀疏点云、密集点云。
优选地，所述视觉任务包括以下任一种：
分类、分割、识别、检测、跟踪等视觉子任务。
优选地，所述对原始三维点云数据进行编解码处理是通过点云编解码器进行的，所述点云编解码器包括以下任一种：
MPEG G-PCC TMC13、MPEG V-PCC TMC2、Draco等。
优选地，所述评价指标是指用于评估、比较视觉任务模型质量及其效果的统计指标，如准确率，精度等。
优选地，还包括：对多视觉任务的综合评价：依据各视觉子任务的评价结果及各视觉任务的重要性对点云数据压缩质量进行综合评价。
优选地，还包括：所述视觉任务包括至少两种的视觉子任务时，依据特定场景下视觉子任务的重要性而对各个不同视觉子任务设定权重，结合各视觉子任务评价结果，得出多任务评价结果。
优选地，还包括：视觉子任务采用以下任意一种评价指标：准确率、错误率、平均精度，召回率、精确率、交并比。
根据本发明提供的一种基于视觉任务的点云数据压缩质量评价系统，包括：
点云数据编解码模块：对输入的原始三维点云数据进行编解码处理，并输出编解码 后的点云数据；
视觉任务处理模块：对未经过和经过编解码的三维点云数据分别依据视觉任务处理算法进行处理，得到处理后的数据；
视觉任务模型评估模块：根据处理后的数据，计算视觉任务的评价指标；
评估结果对比模块：将编解码前后点云数据的评价指标进行对比，依据对比结果对点云数据压缩质量进行评价。
优选地，所述对三维点云数据进行处理指根据视觉任务处理算法进行运算。
优选地，所述原始三维点云数据包括以下任一种：
稀疏点云、密集点云。
优选地，所述视觉任务包括以下任一种或至少两种的自由组合：
分类、分割、识别、检测、跟踪等视觉子任务。
优选地，所述对原始三维点云数据进行编解码处理是通过点云编解码器进行的，所述点云编解码器包括以下任一种：
MPEG G-PCC TMC13、MPEG V-PCC TMC2、Draco等。
优选地，所述评价指标是指用于评估、比较视觉任务模型质量及其效果的统计指标，如准确率，精度等。
优选地，还包括：
多视觉任务的综合评价：依据各视觉子任务的评价结果及各视觉任务的重要性对点云数据压缩质量进行综合评价。
优选地，还包括：所述视觉任务包括至少两种的视觉子任务时，依据特定场景下视觉子任务的重要性而对各个不同视觉子任务设定权重，结合各视觉子任务评价结果，得出多任务评价结果。
优选地，还包括：视觉子任务采用以下任意一种评价指标：准确率、错误率、平均精度，召回率、精确率、交并比。
另外，本发明还提供了一种点云系统，包括：原始三维点云数据输入模块：输入原始三维点云数据；点云数据编解码模块：对输入的原始三维点云数据进行编解码处理，并输出编解码后的三维点云数据；以及上述任意一项所述基于视觉任务的点云数据压缩质量评价系统，即包含视觉任务处理模块：对未经过和经过编解码的三维点云数据分别依据视觉任务处理算法进行处理，得到处理后的数据；视觉任务模型评估模块：根据处理后的数据，计算视觉任务的评价指标；评估结果对比模块：将编解码前后点云数据的 评价指标进行对比，依据对比结果，对点云数据压缩质量进行评价。
在本发明所提供的点云系统中，优选地，可对应地提供如上述中任意一项所述的基于视觉任务的点云数据压缩质量评价系统中进一步优选的技术方案。
与现有技术相比，本发明具有如下的有益效果：
1、本发明根据点云的应用目的，提出了基于视觉任务的点云数据压缩质量评价方法及系统。即针对特定视觉子任务(如点云分类、分割、检测、识别等)，考虑点云在不同应用场景或目的下的点云压缩质量评价方法，并根据编解码前后的点云数据计算得到的特定视觉任务评价指标结果，指导点云压缩质量评价结果。
2、本发明与现有的点云质量评价方法相比，本发明具有针对性，能够根据特定视觉任务衡量点云压缩质量。此外，还能够根据多种视觉任务综合衡量点云压缩质量。
附图说明
通过阅读参照以下附图对非限制性实施例所作的详细描述，本发明的其它特征、目的和优点将会变得更明显：
图1是本发明提供的实施例中一种基于视觉任务的点云数据压缩质量评价方法流程示意图；
图2是本发明提供的实施例中点云系统功能框图示意图。
具体实施方式
下面结合具体实施例对本发明进行详细说明。以下实施例将有助于本领域的技术人员进一步理解本发明，但不以任何形式限制本发明。应当指出的是，对本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变化和改进。这些都属于本发明的保护范围。
根据本发明提供的一种基于视觉任务的点云数据压缩质量评价方法，包括以下步骤：
S1、输入原始三维点云数据；
S2、根据特定视觉任务处理算法对输入的原始三维点云数据进行处理，并得到处理后的数据；
S3、根据S2中得到的处理后的数据，计算该特定视觉任务的某一种或多种评价指标；
S4、使用点云编解码器对输入的原始三维点云数据进行编解码，得到解码后的三维 点云数据；
S5、根据与S2中相同的特定视觉任务处理算法对解码后的三维点云数据进行处理，并得到处理后的数据；
S6、根据S5中得到的处理后的数据计算与S3中相同的该特定视觉任务的评价指标；
S7、对比S3和S6中得到的特定视觉任务的评价分数作为评价结果，并依据评价分数对点云数据压缩质量进行评价。
本发明中，进行对比的评价结果包含评价分数、评价划分等级等用于评价结果的方式，不受具体评价的形式所限制，本实施例中采用评价分数。
优选地，所述原始三维点云数据包括以下任一种：
稀疏点云、密集点云。
优选地，所述特定视觉任务包括以下任一种或至少两种的自由组合：
点云分类、分割、识别、检测、跟踪等视觉子任务。
优选地，所述特定视觉任务的处理算法为：
某一种实现点云特定视觉任务的算法。
优选地，所述编解码器包括以下任一种：
MPEG G-PCC TMC13、MPEG V-PCC TMC2、Draco等。
优选地，还包括：
基于多种视觉子任务对点云压缩质量进行评价，可按照视觉子任务的种类分别执行上述步骤，并综合各任务评价指标对比结果及各视觉子任务的重要性对点云数据压缩质量进行评价。
本发明提供的基于视觉任务的点云数据压缩质量评价系统，可以通过本发明给的基于视觉任务的点云数据压缩质量评价方法的步骤流程实现。本领域技术人员可以将所述基于视觉任务的点云数据压缩质量评价方法，理解为所述基于视觉任务的点云数据压缩质量评价系统的一个优选例。
根据本发明提供的点云系统，该点云系统包含基于视觉任务的点云数据压缩质量评价系统，所述点云系统包括：
原始三维点云数据输入模块：输入原始三维点云数据；
点云数据编解码模块：对输入的原始三维点云数据进行编解码操作，并输出解码后的点云数据；
视觉任务处理模块：根据视觉任务的处理算法对输入的三维点云数据进行处理，并 得到处理后的数据；
视觉任务模型评估模块：根据经过视觉任务算法处理后得到的数据，计算该视觉任务的某一种或多种评价指标。
评估结果对比模块：对比编码前后点云数据在经过视觉任务算法处理后的评价指标，并依据对比结果对点云数据压缩质量进行评价。
优选地，还包括：
所述原始三维点云数据包括以下任一种：稀疏点云、密集点云。
优选地，还包括：
所述视觉任务包括以下任一种：点云分类、分割、识别、检测、跟踪等视觉任务。
优选地，还包括：
所述视觉任务的处理算法为：某一种实现点云视觉任务的算法。
优选地，还包括：
所述编解码器包括以下任一种：MPEG G-PCC TMC13、MPEG V-PCC TMC2、Draco等。
本发明的目的是提供一种基于任务的点云数据压缩质量评价方法及系统，能够根据特定视觉任务及其评价指标指导点云压缩质量评价结果。即针对特定视觉任务(如点云分类、分割、识别等)，考虑点云在不同应用场景或目的下的点云压缩质量评价方法，并根据编解码前后的点云数据计算得到的特定视觉任务评价指标结果，指导点云压缩质量评价结果。
下面通过优选例，对本发明进行更为具体地说明。
以自动驾驶场景为例，激光雷达采集到的点云数据非常庞大。海量点云数据十分不利于计算机下一步的传输及存储，也为后续的工作的开展设置了障碍。依据点云数据类型分类，自动驾驶中激光雷达采集到的点云数据属于稀疏点云数据。因此，充分的压缩处理点云数据就成为必要的处理步骤而凸显出重要地位。
在此场景下，可根据视觉任务需要达到的任务性能，得到能够满足视觉任务性能要求的相应质量的点云。由此，基于视觉任务的点云质量评价方法对于视觉任务场景的点云编码具有实际价值。
优选例1：
以自动驾驶中的激光雷达目标检测任务为例，简述基于视觉任务的点云质量评价方法的应用。
如附图1所示，为本方法的具体流程图，简述如下：取得原始稀疏点云数据后，首 先，使用三维点云目标检测算法对输入的原始稀疏点云数据进行处理，并得到处理后的输出数据。然后，根据输出数据计算三维点云目标检测算法评价指标。常用的三维点云目标检测算法指标有：准确率、召回率、平均精度、交并比等。这里以平均精度一种评价指标为例，并将平均精度记为AP。根据上述使用三维点云目标检测算法对输入的原始稀疏点云数据进行处理后的输出数据，计算得到处理前平均精度AP 1。
其次，选择合适的编解码器对原始稀疏点云数据进行编解码，并输出解码后的点云数据。此例中，采用适合于处理稀疏点云的编解码器MPEG G-PCC TMC13对原始稀疏点云数据进行编解码操作。然后，使用相同的三维点云目标检测算法对解码后的点云数据进行处理，并得到处理后的输出数据。并根据输出数据计算三维点云目标检测算法评价指标，得到处理后平均精度AP 2。
最后，对比上述得到的两个平均精度值即处理前平均精度AP 1和处理后平均精度AP 2，并依据对比结果对点云数据压缩质量进行评价。其中，对比方法可采用比值法或其他对比方法。
优选例2：
针对自动驾驶场景，某些情况下需基于多个视觉子任务进行计算，如本实施例中，将目标检测和目标跟踪该两个视觉子任务纳入点云压缩质量评价的方法或系统中。因此，为进一步降低存储及传输压力，通常会采用同一点云数据完成视觉任务计算。在此场景下，需综合考量点云质量对多种视觉子任务的影响，由此，基于多视觉任务的综合点云质量评价方法尤为重要。具体而言，根据多视觉任务场景下各视觉任务的评价指标对比结果，及表示各视觉子任务重要性的权重，可进一步对点云数据压缩质量进行综合评价。具体方案简述如下：
首先，根据单视觉任务的点云质量评价方法，采用比值法得到N种视觉任务的点云质量评价指标，记为视觉子任务评价结果M i，其中i为视觉任务编号，每个视觉子任务所采用评价指标T,评价指标T 1表明处理前评价结果对应的指标分数值,评价指标T 2表明处理后评价结果对应的指标分数值。
Figure PCTCN2020101786-appb-000001
其次，根据实际情况，为视觉子任务评价结果M i按照各视觉子任务重要性分配权 重，记为权重系数α i，各视觉任务权重的和为1，即
Figure PCTCN2020101786-appb-000002
则多任务评价结果，即多视觉任务的综合评价指标M可表示为
Figure PCTCN2020101786-appb-000003
其中，M越大，表明点云质量越好。
继续说明，上述所列举实施例，针对将目标检测和目标跟踪该两个视觉任务的点云质量评价指标而言，分别记为目标检测评价指标M 1和目标跟踪评价指标M 2。其次，根据实际情况，按照各视觉任务重要性分配权重，各视觉任务权重和为1。在此实施例中，目标检测任务权重设为第一权重系数α 1，目标跟踪任务权重设为第二权重系数α 2，且α 1+α 2＝1。则多视觉任务的综合评价指标M可表示为M＝α 1M 1+α 2M 2。M越大，点云质量越好。
公式(1)和公式(2)中，仅以T作为评价指标的指代字母,在所述视觉任务包括至少两种的视觉子任务时，每个视觉子任务所择一采用的评价指标T之间可以相同也可以不相同、也可以部分相同。依据实际情况，为视觉子任务自由匹配相适应的评价指标。对上述公式(1)可变形来看，评价指标T在不同视觉子任务中从以下指标中选择一个用于评价该个视觉子任务的结果：准确率AC(ACcuracy)、错误率ER(Error Rate)、平均精度AP(Average Precision)，平均精度均值mAP(mean Average Precision)、召回率RC(ReCall)、精确率PR(PRecision)、交并比IoU(Intersection over Union)。
如附图2所示，本实施例还提供了点云系统，该点云系统包含基于视觉任务的点云数据压缩质量评价系统，点云系统包括：原始三维点云数据输入模块、点云数据编解码模块、视觉任务处理模块、视觉任务模型评估模块以及评估结果对比模块。
原始三维点云数据输入模块，用于输入原始三维点云数据；
点云数据编解码模块，用于对输入的原始三维点云数据进行编解码操作，并输出解码后的点云数据；
视觉任务处理模块，用于根据视觉任务的处理算法对输入的三维点云数据进行处理，并得到处理后的数据；
视觉任务模型评估模块，用于根据经过视觉任务算法处理后得到的数据，计算该视觉任务的某一种或多种评价指标。
评估结果对比模块，用于对比编码前后点云数据在经过视觉任务算法处理后的评价指标，并依据对比结果对点云数据压缩质量进行评价。
在本实施中点云系统、基于视觉任务的点云数据压缩质量评价系统，其中，图2中视觉任务处理模块、视觉任务模型评估模块、评估结果对比模块是同一物理部件分别对未处理和处理后的点云数据分别予以功能实现。当然，针对未处理和处理后的点云数据分别采用两套同样的物理部件分别予以功能实现，也属本发明的保护范围之内。
综上可知，本发明的基于视觉任务的点云数据压缩质量评价方法及系统、点云系统，可以针对特定视觉子任务，考虑点云在不同应用场景或目的下的点云压缩质量评价方法，并根据编解码前后的点云数据计算得到的特定视觉任务评价指标结果，指导点云压缩质量评价结果。与现有的点云质量评价方法相比，本发明具有针对性，能够根据特定视觉任务衡量点云压缩的质量。
本发明中，点云数据类型、视觉任务、视觉任务的处理算法、编解码器均可多样，优选地，可以由以下一组可选方案为例实现：
点云数据类型：根据目前主流点云数据划分类型，可将点云数据划分为稀疏点云数据和密集点云数据两类。针对不同场景和需求，可选择相应类型数据进一步处理。
视觉任务：点云分类、分割、识别、检测、跟踪等视觉任务。针对不同场景和需求，可指定一种或多种视觉子任务。
视觉任务的处理算法：根据不同视觉子任务，可采用的相应处理算法。如针对三维点云目标识别任务，Second/PointRCNN/PointPillars等算法都是可选的。
编解码器：根据场景需求和点云数据类型，可选择合适的编解码器。如针对稀疏点云，MPEG G-PCC TMC13/Draco等为可选编解码器。针对密集点云，MPEG V-PCC TMC2等为可选编解码器。
本发明中基于视觉任务的点云数据压缩质量评价方法，包含能够针对不同类型的原始三维点云数据、不同视觉子任务、不同编码器、不同任务指标进行自由组合的方案，
具体而言，点云数据类型包含：密集点云、稀疏点云。任选以下点云编解码器：MPEG G-PCC TMC13、MPEG V-PCC TMC2、Draco等。视觉任务包含：对点云目标进行分类、分割、检测、识别、跟踪或其他视觉任务的处理。视觉任务性能评价指标包含：准确率、错误率、平均精度，召回率、精确率、交并比、平均精度均值。
本发明中，对单个视觉子任务从评价指标中择一进行评价，例如表1中，对于密集点云采用MPEG G-PCC TMC13编解码器进行处理，基于目标分类的视觉子任务用于评价 时，从视觉任务性能评价指标中择一进行评价，即例如从准确率、错误率、平均精度中选择准确率。
针对多个视觉子任务进行评价时，例如在表1中，对于稀疏点云采用Draco编解码器进行处理，包含目标检测和目标识别两个视觉子任务，则针对每个视觉子任务分别择一选择视觉任务性能评价指标用于评价，此处选择不同，即从交并比、平均精度均值等中选择交并比用于评价目标检测、平均精度均值用于评价目标识别。又或者，针对每个视觉子任务选择相同的评价指标用于评价，此处选择相同，即从交并比、平均精度均值等中选择交并比来评价目标检测和目标识别。
本发明可依据具体实际场景进行最优化的选择，也并非表1中有限组合方式所限制，针对密集点云、稀疏点云，采用相适应的点云编解码器，基于单个或者多个视觉子任务分别予以处理，用于评价每个视觉子任务时所采用评价指标T可以相同也可以不相同、也可部分相同，得到处理前后的对比评价结果。
Figure PCTCN2020101786-appb-000004
表1：自由组合示例表
依据实际评价需求而上述组合后，对评价结果予以衡量的指标及范围包含：经过一种编码器编码后和未压缩点云数据分别在经过视觉任务算法处理后得到的2个相同类型的视觉任务性能评价指标的比值(例如公式(1)可知)或其他对比方法。
例如，当评价指标选择准确率且选用评价方法为比值法时，则基于视觉任务的点云质量评价指标为经过一种编码器编码后和未压缩点云数据分别在经过视觉任务算法处理后得到的2个准确率AC(ACcuracy)的比值。其比值范围在0-1之间，值越大，在视觉任务场景下对应的点云质量越好。
需要注意的是，发明中只是以上述方法为例对基于视觉任务的点云数据压缩质量评价方法及系统、点云系统进行说明，并不局限于以上的点云数据类型、视觉任务、 视觉任务的处理算法及编解码器等选择。
本实施中所提供的基于视觉任务的点云数据压缩质量评价系统中各个功能模块与上述实施例中基于视觉任务的点云数据压缩质量评价方法所分别相对应，那么装置中所具有的结构和技术要素可由生成方法相应转换形成，在此省略说明不再赘述。
本发明虽然已以较佳实施例公开如上，但其并不是用来限定本发明，任何本领域技术人员在不脱离本发明的精神和范围内，都可以利用上述揭示的方法和技术内容对本发明技术方案做出可能的变动和修改，因此，凡是未脱离本发明技术方案的内容，依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化及修饰，均属于本发明技术方案的保护范围。
本领域技术人员知道，除了以纯计算机可读程序代码方式实现本发明提供的系统、装置及其各个模块以外，完全可以通过将方法步骤进行逻辑编程来使得本发明提供的系统、装置及其各个模块以逻辑门、开关、专用集成电路、可编程逻辑控制器以及嵌入式微控制器等的形式来实现相同程序。所以，本发明提供的系统、装置及其各个模块可以被认为是一种硬件部件，而对其内包括的用于实现各种程序的模块也可以视为硬件部件内的结构；也可以将用于实现各种功能的模块视为既可以是实现方法的软件程序又可以是硬件部件内的结构。
以上对本发明的具体实施例进行了描述。需要理解的是，本发明并不局限于上述特定实施方式，本领域技术人员可以在权利要求的范围内做出各种变化或修改，这并不影响本发明的实质内容。在不冲突的情况下，本申请的实施例和实施例中的特征可以任意相互组合。

Claims (16)

  1. 基于视觉任务的点云数据压缩质量评价方法，其特征在于，包括：
    输入原始三维点云数据；
    对所述原始三维点云数据进行处理，根据处理后的第一数据，计算视觉任务的第一评价指标；
    对所述原始三维点云数据进行编解码，将编解码后的三维点云数据进行处理，根据处理后的第二数据，计算所述视觉任务的第二评价指标；
    对比第一评价指标与第二评价指标得出评价结果，依据评价结果对点云数据压缩质量进行评价。
  2. 根据权利要求1所述的基于视觉任务的点云数据压缩质量评价方法，其特征在于，所述对三维点云数据进行处理或对编解码后的三维点云数据进行处理指根据视觉任务处理算法进行运算。
  3. 根据权利要求1所述的基于视觉任务的点云数据压缩质量评价方法，其特征在于，所述原始三维点云数据为稀疏点云或密集点云。
  4. 根据权利要求1所述的基于视觉任务的点云数据压缩质量评价方法，其特征在于，所述视觉任务包括以下任意一种或至少两种的自由组合的视觉子任务:分类、分割、识别、检测、跟踪。
  5. 根据权利要求4所述的基于视觉任务的点云数据压缩质量评价方法，其特征在于，所述视觉任务包括至少两种的视觉子任务时，依据特定场景下视觉子任务的重要性而对各个不同视觉子任务设定权重，结合各视觉子任务评价结果，得出多任务评价结果。
  6. 根据权利要求1所述的基于视觉任务的点云数据压缩质量评价方法，其特征在于，视觉子任务采用以下任意一种评价指标：准确率、错误率、平均精度，召回率、精确率、交并比。
  7. 根据权利要求1所述的基于视觉任务的点云数据压缩质量评价方法，其特征在于，所述对原始三维点云数据进行编解码处理是通过点云编解码器进行的，所述点云编解码器为MPEG G-PCC TMC13或MPEG V-PCC TMC2或Draco。
  8. 根据权利要求1所述的基于视觉任务的点云数据压缩质量评价方法，其特征在于，还包括对多视觉任务的综合评价：依据各视觉任务的评价结果及各视觉任务的重要性对点云数据压缩质量进行综合评价。
  9. 基于视觉任务的点云数据压缩质量评价系统，其特征在于，包括：
    视觉任务处理模块：对未经过和经过编解码的三维点云数据分别依据视觉任务处理算法进行处理，得到处理后的数据；
    视觉任务模型评估模块：根据处理后的数据，计算视觉任务的评价指标；
    评估结果对比模块：将编解码前后点云数据的评价指标进行对比，依据对比结果对点云数据压缩质量进行评价。
  10. 根据权利要求9所述的基于视觉任务的点云数据压缩质量评价系统，其特征在于，所述原始三维点云数据为稀疏点云或密集点云。
  11. 根据权利要求9所述的基于视觉任务的点云数据压缩质量评价系统，其特征在于，所述视觉任务包括以下任意一种或至少两种的自由组合的视觉子任务:分类、分割、识别、检测、跟踪。
  12. 根据权利要求11所述的基于视觉任务的点云数据压缩质量评价系统，其特征在于，所述视觉任务包括至少两种的视觉子任务时，依据特定场景下视觉任务的重要性而对各个不同视觉子任务设定相对应的权重，并结合各视觉子任务评价结果，得出多任务评价结果。
  13. 根据权利要求9所述的基于视觉任务的点云数据压缩质量评价系统，其特征在于，视觉任务采用以下任意一种评价指标：准确率、错误率、平均精度、平均精度均值、召回率、精确率、交并比。
  14. 根据权利要求9所述的基于视觉任务的点云数据压缩质量评价系统，其特征在于，所述对原始三维点云数据进行编解码处理是通过点云编解码器进行的，所述点云编解码器为MPEG G-PCC TMC13或MPEG V-PCC TMC2或Draco。
  15. 根据权利要求9所述的基于视觉任务的点云数据压缩质量评价系统，其特征在于，还包括对多视觉任务的综合评价：依据各视觉任务的评价结果和各视觉任务的重要性对点云数据压缩质量进行综合评价。
  16. 一种点云系统，其特征在于，包括：
    原始三维点云数据输入模块：输入原始三维点云数据；
    点云数据编解码模块：对输入的原始三维点云数据进行编解码处理，并输出编解码后的三维点云数据；以及
    如权利要求9到15中任意一项所述的基于视觉任务的点云数据压缩质量评价系统。
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