WO2019056306A1 - Procédé et système de détection de plan basé sur des informations de profondeur robustes - Google Patents

Procédé et système de détection de plan basé sur des informations de profondeur robustes Download PDF

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Publication number
WO2019056306A1
WO2019056306A1 PCT/CN2017/102947 CN2017102947W WO2019056306A1 WO 2019056306 A1 WO2019056306 A1 WO 2019056306A1 CN 2017102947 W CN2017102947 W CN 2017102947W WO 2019056306 A1 WO2019056306 A1 WO 2019056306A1
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plane
detection
growth
seed block
effective
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PCT/CN2017/102947
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English (en)
Chinese (zh)
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金枝
罗海丽
周长源
邹文斌
李霞
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深圳大学
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Priority to PCT/CN2017/102947 priority Critical patent/WO2019056306A1/fr
Publication of WO2019056306A1 publication Critical patent/WO2019056306A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras

Definitions

  • the invention belongs to the field of image processing and computer vision, and in particular relates to a robust depth information based plane detection method and system.
  • the plane detection technique can be used for 3D reconstruction.
  • 3D reconstruction can be simply summarized as the process of plane detection of indoor and outdoor scenes and the establishment of a segmentation plane model.
  • planar inspection technology is also widely used for object detection in robot navigation systems and computer vision.
  • the texture information of the plane was mainly used, but when the plane color or texture is inconsistent, it will bring great challenges to this method.
  • the distance information of the depth map is used to solve the above problem, and the result also proves that the method can effectively cope with the complicated situation.
  • the depth map can be generated directly by a depth camera (such as SwissRanger SR40001, Microsoft Kinect) or synthesized by software.
  • the different values in the graph reflect the distance information of the object in the scene relative to the camera. Since the depth map represents spatial information for each point in the scene, points from the same plane will have similar spatial characteristics, such as gradients and normals.
  • the plane detection methods can be divided into three categories: iterative plane fitting method, based on Hough transform method and region growing method.
  • the iterative plane fitting method is a commonly used method for plane detection, and its typical representative is RANSAC, in which the fitting model is initialized according to several randomly selected points in RANSAC.
  • the method has better effect in detecting large planes and is robust to noise, but the calculation amount is too large, and Complex planes are oversimplified during the calculation process.
  • the Hough transform method is often used for parameterized target detection, especially for lines and circles in 2D planes. In order to make this type of method available in 3D space and reduce computational cost, a variety of Hough transform-based derivative algorithms have emerged.
  • the 3D Hough transform method uses the slope of the plane in the x-axis and y-axis directions and its distance from the origin of the coordinate to represent the plane, but it also has a higher computational cost when looking for the parameters of the fitted model, especially when This problem becomes more pronounced when the input data is large or the accumulator is sensitive.
  • the Random Hough Transform uses a probabilistic model to calculate parameters to avoid high computational costs when finding optimal parameters.
  • the main idea of the regional growth method is to use the correlation between adjacent points to construct the plane.
  • the work proposes an algorithm based on the growth of two seed points.
  • the plane parameters are gradually updated by the centroid and covariance matrix of the grown region, but the calculation is too big.
  • CORG Cached-Octree Region-Growing
  • two growth strategies are proposed: a sub-window growth algorithm and a hybrid growth algorithm in an unstructured environment in a point cloud of a structured environment. This algorithm is faster than point-based growth when the window size is set appropriately.
  • RPCA Robust Principle Component Analysis
  • the process of plane growth begins with a bin, and if the angle between the growing bin and its neighboring bin is less than a certain threshold, the neighboring bin is included in the currently growing plane.
  • the method works normally when the angle between adjacent bins is an acute angle, but the method may fail when the angle between adjacent bins is an obtuse angle.
  • the technical problem to be solved by the present invention is to provide a robust depth information based plane detection method and system, aiming at solving the problem that the existing plane detection algorithm has poor accuracy and robustness in complex scenarios.
  • the present invention is implemented in such a manner that a robust depth information based plane detection method includes:
  • a depth map detection plane containing the effective growth plane of the valid seed block is output.
  • the invention also provides a robust depth information based plane detection system, comprising:
  • An extracting unit configured to receive a depth map, and extract a number of valid seed blocks in the depth map
  • a growth unit for performing region growth according to the effective seed block to obtain a generation plane of the effective seed block
  • a correcting unit configured to perform growth correction or undergrowth correction on a generating plane of the effective seed block to obtain an effective growth plane of the effective seed block
  • an output unit configured to output a depth map detection plane including an effective growth plane of the effective seed block.
  • the present invention has the beneficial effects that the embodiment of the present invention obtains the growth plane of each effective seed block by extracting the effective seed block of the depth map and performing region growing according to the extracted effective seed block, for each effective seed.
  • the growth plane of the block undergoes growth correction or undergrowth correction to obtain an effective growth plane, and the depth map is detected according to the effective growth plane.
  • the embodiment of the invention improves the accuracy and robustness of the plane detection method by performing growth correction or undergrowth correction on the growth plane obtained by growing the effective seed block region.
  • FIG. 1 is a flowchart of a robust depth information based plane detection method according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of an adjacent point set according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of adjacent points of an effective seed block according to an embodiment of the present invention.
  • FIG. 5 is a flowchart of an undergrowth correction method according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of a robust depth information based plane detection system according to an embodiment of the present invention.
  • FIG. 1 shows a robust depth information based plane detection method provided by an embodiment of the present invention, including:
  • S101 Receive a depth map, and extract a number of valid seed blocks in the depth map.
  • S102 Perform area growing according to the effective seed block to obtain a generating plane of the effective seed block.
  • the embodiment of the present invention provides a robust Depth-driven Plane Detection (DPD) method. That is, the depth detection is used for plane detection.
  • the plane detection method includes two parts: plane detection based on seed block growth and post-processing process to further enhance the robustness of the algorithm.
  • the plane detection method starts from the seed block with the highest smoothness, and uses the plane equation of the fitting plane of the growth plane and the dynamic threshold function to guide the growth process. Under the action of this mechanism, when the seed block grows to the maximum extent, the next seed block begins to grow, and the growth process is iterated until all the planes are detected.
  • the accuracy and robustness of the proposed method can be improved by using the dynamic threshold function and the post-processing process of the enhancement mechanism. The latter is proposed for the planar overgrowth and undergrowth problems that are easy to occur based on the growth-based planar detection method.
  • a key step in region growth is to select growing seed pieces, ie, to select areas without holes.
  • an L ⁇ L rectangular window is slid one pixel at a time in a raster scan manner to traverse the depth map, and the rectangular window is checked at the position of each pixel point. Whether all the points are hole points, when there is no hole point in the rectangular window, the point set in the rectangular window is regarded as a valid seed block, and is calculated by linear least square plane fitting method (Linear Least Squares, LLS)
  • LLS Linear Least Squares
  • the plane can be represented by the usage vector n and the distance d from the origin of the coordinate system.
  • the fitting plane of the effective seed block can be obtained by linear least squares plane fitting method.
  • S the plane equation of the effective seed block
  • n i represents the normal vector of the fitting plane of the i-th effective seed block
  • d i represents the distance between the fitting plane of the i-th effective seed block and the origin of the coordinate system.
  • the root mean square fitting error ⁇ i is defined as:
  • P represents the set of points of all pixels in the valid seed block
  • represents the number of pixels in the valid seed block
  • the root mean square fitting error ⁇ i is used to represent the smoothness of the effective seed block, so a smaller root mean square fitting error ⁇ i means higher smoothness. Since the planar growth process starts with the most smooth effective seed block, accurate growth of the plane can be ensured, so in this embodiment, all the effective seed blocks are sorted from small to large according to the root mean square fitting error ⁇ i .
  • Regional growth process Regional growth is an iterative process that generates planes from an effective seed block. In the specific growth process, not all effective seed blocks will eventually have the opportunity to grow, because the regional growth begins with the smoothest effective seed block, and the growth of the effective seed block used for growth will result in the inclusion of growth planes. With some valid seed blocks that have not been used, these included effective seed blocks do not have the opportunity to grow.
  • the key to the region-based growth method is to distinguish the inner point and the outer point of the current growth plane, and the judgment is based on the threshold T, wherein the threshold T is the output of the dynamic threshold function proposed by the embodiment of the present invention.
  • the adjacent point set of the growth plane is an empty set, or there is no point in the adjacent point set suitable for the current growth plane, it is determined that the current plane growth reaches the maximum range, and the current plane ends the growth.
  • the adjacent point set refers to a set of all pixel points adjacent to the current growth plane, that is, points 1 to 16 constitute a set of adjacent points of the current growth plane.
  • the main contents of the growth process include:
  • the adjacent side of the effective seed block is taken as the center, and the adjacent side of the 3 ⁇ 3 rectangular window is taken.
  • Point a total of 8 adjacent points, point 1 to point 8.
  • the corresponding fitting error is obtained by p+d i
  • the fitting error is less than the threshold T, the adjacent point is merged into the current growth plane, and conversely, the adjacent point is regarded as the outer point of the current growth plane. In this phase, a fitting error needs to be calculated for each adjacent point.
  • the linear least squares plane fitting method LLS is used to update the parameters of the plane equation, and the root mean square fitting error is also updated.
  • a growth plane has been fitted and the fitting error is calculated.
  • LLS linear least squares plane fitting method
  • the planar plane can be optimally adjusted again and again during the growth process.
  • the plane equation is updated once after each growth, and a root mean square fitting error is also calculated for all points on the plane.
  • the embodiment of the present invention designs a dynamic threshold function based on a noise model and a plane size.
  • the dynamic threshold function is defined as follows:
  • I represents the depth map
  • I d represents a depth value of a point on the depth map that is substituted into a plane equation using a fitting error formula to calculate a fitting error.
  • the point is the adjacent of the current growth plane.
  • ⁇ represents the maximum allowable roughness of the growth plane
  • ⁇ determines the growth rate of the threshold
  • H and W represent the height and width of the depth map, respectively
  • ⁇ and k are constants
  • j represents the number of iterations of the plane during the growth process
  • initialization order j 1
  • the maximum threshold T of the output is determined by I d .
  • the dynamic threshold function can solve the problem based on the noise model well. For example, when the growth plane is the distal facet, the overgrowth caused by the large threshold can be avoided because the growth plane is considered; when the growth plane is parallel to the camera In the plane, the threshold will not be a fixed value due to the accumulation of noise taking into account the growth process.
  • the post-processing process of the mandatory mechanism includes an over-growth correction process and an under-growth correction process, and the over-growth correction process and the under-growth correction process are respectively described below:
  • one of the growth planes preferentially grows to its intersection with another growth plane.
  • Overgrowth refers to the phenomenon that the current growth plane erroneously grows onto the intersecting plane if the fitting error between the adjacent pixel points of the plane intersection line and the current plane is smaller than the current threshold.
  • the growth direction can be divided into longitudinal growth and lateral growth.
  • intersection line can be expressed as a parametric equation:
  • ⁇ represents the cross product of the two vectors
  • t represents the unknown parameter
  • p 0 is a point on the intersection line, defined as:
  • the main contents of the correction process include:
  • the overgrowth region S 0 is accurately detected.
  • the degree of overgrowth depends mainly on the angle of the intersection plane, the accuracy of the depth data, and the threshold T, where the angle ⁇ is the inverse cosine of the two-plane normal vector point multiplication, and the threshold T is the output of the dynamic threshold function.
  • the theoretical width w of the overgrowth region is equal to the ratio of the threshold value T to the sine value of the angle ⁇ .
  • the embodiment of the present invention takes the actual width as [w] (1+). ⁇ ), where [w] represents a larger integer close to w, ⁇ >0, which ensures that the actual width is greater than the theoretical width.
  • Redistribution refers to the plane equation that substitutes the point on the farthest boundary of the overgrowth region S 0 into two planes, and calculates the fitting error of the point to the plane respectively, compares the two fitting error magnitudes, and merges the overgrowth region S 0 To the plane with a small fitting error.
  • the current detection plane S i is overgrowth according to the overgrowth region S 0 . If over growth, the overgrowth region S 0 is merged with the adjacent plane, and conversely, the overgrowth region S 0 is merged with the detection plane S i . In this step, if the point on the overgrowth region S o is substituted into the adjacent plane, it is found that the fitting error is smaller than the fitting error substituted into the current detection plane, indicating that the overgrowth region should belong to the adjacent plane, so the current A growth problem has occurred in the detection plane.
  • the overgrowth area S o may be broken into small isolated blocks to detect and redistribute these small isolated blocks.
  • a primary, secondary overgrowth region is created in the detection of the previous step (5), and after the main region has been reallocated, the secondary region may become an isolated region. Redistribution is based on substituting points in isolated regions into adjacent plane equations. The plane with the smallest root mean square fitting error is the plane to which the isolated region belongs.
  • the embodiment of the present invention proposes a method of plane merging, that is, when the two growth planes satisfy the three conditions of parallel, coplanar, and adjacent, they can be merged into one larger plane. These relationships are detected in sequence in the method of under-growth correction.
  • Coplanar Calculate the fitting error of each point on the two growth planes to the detection plane, that is, the fitting error of the point of the smaller growth plane in the two growth planes to the plane of the larger growth, and the larger growth plane From the point above to the fitting error of the self, when the calculated Hellinger distance of the two fitting errors is less than the threshold, the two growth planes are discriminated.
  • the Hellinger distance is usually used to measure the similarity of two probability distributions. However, if the size of the two growth planes is too different, even if the two growth planes are coplanar in real conditions, a large Hellinger distance can be obtained, so for the two growth planes with large differences in size, the judgment threshold Tm is used. Determine whether it is coplanar.
  • Adjacent After expanding the current growth plane by one pixel, it is detected that its edge has an intersection with the edge of another growth plane, and the two growth planes are adjacent planes.
  • the detection planes parallel to the detection plane S u are arranged in descending order according to the size of the area. Specifically, in over-growth correction and under-growth correction, the principle of correction is to start from a plane with a large area. The plane equation is more accurate due to the larger area.
  • S k denotes any detection plane parallel to S u , assuming that the plane pairs S k and S u are parallel, respectively calculating the fitting error of each pixel point on the detection plane S k and the detection plane S u Ku , and the fitting error e uu of each pixel point on the detection plane S u ;
  • the two fitting errors in the step (5) are represented by a histogram, and the Hellinger distance of the two curves in the histogram is calculated. Determining whether the hellinger distance is smaller than a hellinger distance determination threshold T h , and when it is smaller than a hellinger distance determination threshold T h , then S k is coplanar with S u , and if not smaller, S k and S u are not coplanar, the hellinger
  • the distance judgment threshold T h is a constant;
  • the fitting error e ku of each pixel point on the detection plane S k and the detection plane S u is calculated.
  • the fitting error e ku is smaller than the judgment threshold T m , then S k is coplanar with S u , and if not smaller, S k and S u are not coplanar, and the judgment threshold T m is constant.
  • the above embodiment provided by the present invention uses a depth information to drive a plane detection model to detect a depth map generated by a depth camera using a seed growth method, and post-processes a plane on which the seed block is grown, so that each Plane detection of indoor scenes and depth noise is more robust, improving the accuracy of plane detection.
  • the post-processing of the enhancement mechanism can re-allocate the correct boundary points to the plane.
  • the above embodiments provided by the present invention can be applied to fields such as robust planar detection (wall surface, desktop, etc.), indoor scene reconstruction and position recognition, robot navigation systems, and object recognition in the field of computer vision.
  • FIG. 6 shows a robust depth information based plane detection system provided by an embodiment of the present invention, including:
  • An extracting unit 601 configured to receive a depth map, and extract a number of valid seed blocks in the depth map
  • a growing unit 602 configured to perform region growing according to the effective seed block, to obtain a generating plane of the effective seed block
  • a correcting unit 603 configured to perform a growth correction or an undergrowth correction on a generating plane of the effective seed block to obtain an effective growth plane of the effective seed block;
  • the output unit 604 is configured to output a depth map detection plane including an effective growth plane of the effective seed block.
  • extracting unit 601 is specifically configured to:
  • the point set in the rectangular window is used as a valid seed block
  • the plane equation and smoothness of each of the effective seed blocks are calculated by a linear least squares plane fitting method.
  • the extraction unit is also used to:
  • n i denote the normal vector of the fitting plane of the i-th effective seed block
  • d i denote the distance between the fitting plane of the i-th effective seed block and the origin of the coordinate system
  • e i (p) represents the fitting error of the fitting plane of the i-th effective seed block and the point p on the i-th effective seed block
  • the order is sorted in order from small to large.
  • growth unit 602 is specifically configured to:
  • the growth is stopped, and a growth plane of the effective seed block is obtained;
  • the threshold T is an output of a dynamic threshold function
  • the dynamic threshold function is:
  • I denotes the depth map
  • I d denotes a depth value of a point on the depth map that is substituted into a plane equation using a fitting error formula to calculate a fitting error
  • ⁇ denotes an allowable maximum roughness of the growth plane
  • ⁇ determines a growth rate of the threshold
  • H and W represent the height and width of the depth map, respectively
  • ⁇ and k are constants
  • j represents the number of iterations of the growth plane during growth
  • the initialization order j 1
  • the maximum threshold T of the output is determined by I d .
  • step of correcting the growth plane of the effective seed block by the correcting unit 603 includes:
  • the step of correcting the under-growth correction of the generating plane of the effective seed block by the correcting unit 603 includes:
  • all the detection planes are arranged in descending order according to the size of the area, and the detection plane S u having the largest area is found;
  • S k and S u represents any of a parallel detection plane, respectively, calculated for each pixel on each pixel on the S k and S u fitting error e ku, S u and the fitting error itself e uu ;
  • the fitting error e ku and the fitting error e uu are represented by a histogram, and the hellinger distance of the two curves in the histogram is calculated;
  • a hellinger distance determination threshold T h Determining whether the hellinger distance is smaller than a hellinger distance determination threshold T h ; if less than, S k is coplanar with S u , if not less, then S k and S u are not coplanar, and the hellinger distance determination threshold T h is constant ;
  • the detection plane S u is a large plane, it is determined whether the fitting error e ku is smaller than the determination threshold T m , and if less than, S k is coplanar with S u , and if not smaller, S k is not shared with S u
  • the judgment threshold T m is a constant;
  • S k is coplanar with S u , it is necessary to further determine whether S k and S u are adjacent. If adjacent, then S k and S u are combined to obtain an effective growth plane, and the plane equation of S u is updated, and The parallel plane of S k is added to the parallel group of S u ;
  • the next detection plane is detected according to the order of the area of all the detection planes until all the detection planes are corrected.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

La présente invention peut être appliquée au traitement d'images. Elle concerne un procédé de détection de plan basé sur des informations de profondeur robustes, qui consiste à : recevoir une image de profondeur, et extraire de l'image de profondeur une pluralité de blocs germes valides ; mettre en oeuvre une opération de croissance de région en fonction du bloc germe valide de façon à obtenir un plan de croissance du bloc germe valide ; mettre en oeuvre une correction de surcroissance ou une correction de sous-croissance sur le plan de croissance du bloc germe valide de façon à obtenir un plan de croissance valide du bloc germe valide ; et produire un plan de détection d'image de profondeur contenant le plan de croissance valide des blocs germes valides. Dans un mode de réalisation de la présente invention, une correction de croissance excessive ou une correction de croissance insuffisante est mise en oeuvre sur un plan de croissance obtenu par la réalisation d'une opération de croissance de région sur un bloc germe valide, ce qui permet d'accroître la précision et la robustesse d'un procédé de détection de plan.
PCT/CN2017/102947 2017-09-22 2017-09-22 Procédé et système de détection de plan basé sur des informations de profondeur robustes WO2019056306A1 (fr)

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Citations (6)

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US20120196679A1 (en) * 2011-01-31 2012-08-02 Microsoft Corporation Real-Time Camera Tracking Using Depth Maps
US8831336B2 (en) * 2011-11-11 2014-09-09 Texas Instruments Incorporated Method, system and computer program product for detecting an object in response to depth information
CN105359187A (zh) * 2013-06-11 2016-02-24 微软技术许可有限责任公司 使用深度相机数据的高性能平面检测
CN105631868A (zh) * 2015-12-25 2016-06-01 清华大学深圳研究生院 一种基于图像分类的深度信息提取方法
CN106060529A (zh) * 2016-06-01 2016-10-26 十二维度（北京）科技有限公司 视频2d转3d的深度图跟踪生成方法及装置
WO2017023456A1 (fr) * 2015-08-05 2017-02-09 Intel Corporation Procédé et système de détection de surface plane à des fins de traitement d'image

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120196679A1 (en) * 2011-01-31 2012-08-02 Microsoft Corporation Real-Time Camera Tracking Using Depth Maps
US8831336B2 (en) * 2011-11-11 2014-09-09 Texas Instruments Incorporated Method, system and computer program product for detecting an object in response to depth information
CN105359187A (zh) * 2013-06-11 2016-02-24 微软技术许可有限责任公司 使用深度相机数据的高性能平面检测
WO2017023456A1 (fr) * 2015-08-05 2017-02-09 Intel Corporation Procédé et système de détection de surface plane à des fins de traitement d'image
CN105631868A (zh) * 2015-12-25 2016-06-01 清华大学深圳研究生院 一种基于图像分类的深度信息提取方法
CN106060529A (zh) * 2016-06-01 2016-10-26 十二维度（北京）科技有限公司 视频2d转3d的深度图跟踪生成方法及装置

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