CN109408459A - 3D data processing method, device, computer equipment and storage medium - Google Patents
3D data processing method, device, computer equipment and storage medium Download PDFInfo
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Abstract
This application involves a kind of 3D data processing method, device, computer equipment and storage mediums.The described method includes: the cryptographic Hash of 3D data block is calculated in the data information based on 3D data block；3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block；The 3D data block is handled to obtain the first 3D data group in calculate node；It merges the first 3D data group to obtain the 2nd 3D data group；By the 2nd 3D data group storage into Hadoop distributed file system.Above-mentioned 3D data processing method, device, computer equipment and storage medium, the cryptographic Hash of 3D data block is first calculated, 3D data block is assigned to corresponding calculate node further according to the cryptographic Hash of 3D data block, merged by processing and associated 3D data block is merged into 3D data group, and it stores and is handled into Hadoop distributed file system, the processing speed and treatment effeciency to 3D data are substantially increased, the excessive problem of computer load is avoided.
Description
Technical field
This application involves three-dimensional printing technology fields, set more particularly to a kind of 3D data processing method, device, computer
Standby and storage medium.
Background technique
Spatial digitizer is used to acquire the geometrical construction and appearance data of simultaneously object analysis or environment, and by collected number
According to three-dimensional reconstruction is carried out, the three-dimensional digital model of scanned object is obtained.These three-dimensional digital models will be applied to industry and set
Meter, Defect Detection, reverse-engineering, robot guiding, landforms measurement, medical information, biological information, criminal identification, digital historical relic
The technical fields such as classical collection, motion picture production, game creation material.
With the development of 3-D technology, in 3-D technologies such as progress 3D scanning, 3D printings in application, producing increasingly
More 3D data, these 3D Data Data amounts are big and data include point side face, and lack has these a large amount of 3D data at present
Processing method is imitated, be easy to cause when in face of mass data that processing speed is slow, treatment effeciency is low, causes computer load is excessive to ask
Topic.
Summary of the invention
Based on this, it is necessary to for the effective treating method lacked at present to these a large amount of 3D data, be easy to cause face
Processing speed is slow when to mass data, treatment effeciency is low, leads to the problem that computer load is excessive, providing one kind being capable of 3D data
Processing method, device, computer equipment and storage medium.
A kind of 3D data processing method, which comprises
The cryptographic Hash of 3D data block is calculated in data information based on 3D data block；
3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block；
The 3D data block is handled in calculate node to obtain at least one the first 3D data group；
It merges the first 3D data group to obtain the 2nd 3D data group；
By the 2nd 3D data group storage into Hadoop distributed file system.
The cryptographic Hash of 3D data block is calculated in the data information based on 3D data block in one of the embodiments,
Include:
Types of models, vertex quantity, dough sheet quantity, open state, length, width and height size, center point based on 3D data block
At least one of set the cryptographic Hash that 3D data block is calculated in data information.
3D data block is assigned to corresponding by the cryptographic Hash based on the 3D data block in one of the embodiments,
Calculate node includes:
3D data block is assigned on corresponding computer by the cryptographic Hash based on the 3D data block.
It is described in one of the embodiments, that the 3D data block is handled to obtain at least one in calculate node
First 3D data group includes:
3D data block is merged, is spliced, three-dimensional reconstruction, grid generate, optimization at least one of operation to obtain
At least one the first 3D data group.
The Hadoop distributed file system is used for according to the 2nd 3D data group in one of the embodiments,
Size is respectively set corresponding space size and redundancy backup quantity and stores the 2nd 3D data group.
The Hash of 3D data block is calculated in the data information based on 3D data block in one of the embodiments,
Before value, further includes:
Analytical calculation is associated to 3D data block, 3D data block is integrated to being mutually related.
It is described in one of the embodiments, that the 3D data block is handled to obtain at least one in calculate node
The first 3D data group is merged to obtain the 2nd 3D data group to be based on MapReduce mould by the first 3D data group with described
What type was realized.
A kind of 3D data processing equipment, described device include:
The cryptographic Hash of 3D data block is calculated for the data information based on 3D data block for cryptographic Hash computing module；
3D data block is assigned to corresponding calculate node for the cryptographic Hash based on the 3D data block by distribution module；
Mapping value computing module handles the 3D data block in calculate node to obtain at least one the first 3D number
According to group；
Merging module merges the first 3D data group to obtain the 2nd 3D data group；
Memory module, for storing the 2nd 3D data group into Hadoop distributed file system.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
Device performs the steps of when executing the computer program
The cryptographic Hash of 3D data block is calculated in data information based on 3D data block；
3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block；
The 3D data block is handled in calculate node to obtain at least one the first 3D data group；
It merges the first 3D data group to obtain the 2nd 3D data group；
By the 2nd 3D data group storage into Hadoop distributed file system.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
It is performed the steps of when row
The cryptographic Hash of 3D data block is calculated in data information based on 3D data block；
3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block；
The 3D data block is handled in calculate node to obtain at least one the first 3D data group；
It merges the first 3D data group to obtain the 2nd 3D data group；
By the 2nd 3D data group storage into Hadoop distributed file system.
Above-mentioned 3D data processing method, device, computer equipment and storage medium, are first calculated the Hash of 3D data block
Value, is assigned to corresponding calculate node for 3D data block further according to the cryptographic Hash of 3D data block, merges and will be associated by handling
3D data block merge into 3D data group, and store and handled into Hadoop distributed file system, substantially increase pair
The processing speed and treatment effeciency of 3D data avoid the excessive problem of computer load.
Detailed description of the invention
Fig. 1 is the flow diagram of 3D data processing method in one embodiment；
Fig. 2 is the structural block diagram of 3D data processing equipment in one embodiment；
Fig. 3 is the internal structure chart of computer equipment in one embodiment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
Spatial digitizer is used to acquire the geometrical construction and appearance data of simultaneously object analysis or environment, and by collected number
According to three-dimensional reconstruction is carried out, the three-dimensional digital model of scanned object is obtained.These three-dimensional digital models will be applied to industry and set
Meter, Defect Detection, reverse-engineering, robot guiding, landforms measurement, medical information, biological information, criminal identification, digital historical relic
The technical fields such as classical collection, motion picture production, game creation material.
With the development of 3-D technology, in 3-D technologies such as progress 3D scanning, 3D printings in application, producing increasingly
More 3D data, these 3D Data Data amounts are big and data include point side face, and lack has these a large amount of 3D data at present
Processing method is imitated, be easy to cause when in face of mass data that processing speed is slow, treatment effeciency is low, causes computer load is excessive to ask
Topic.
Referring to Fig. 1, Fig. 1 is the flow diagram of 3D data processing method of the invention.
In the present embodiment, the 3D data processing method includes:
Step 100, the cryptographic Hash of 3D data block is calculated in the data information based on 3D data block.
In the present embodiment, the step 100 include types of models based on 3D data block, vertex quantity, dough sheet quantity,
The cryptographic Hash of 3D data block is calculated in one of open state, length, width and height size, center position or a variety of data informations.
Specifically, the cryptographic Hash of 3D data block calculates the following information of the 3D data block comprehensively considered: aspect of model A is opened up
Flutter the quantity N and bounding box information B of unit, wherein the aspect of model A includes:
A) types of models: point cloud or triangle gridding or quadrilateral mesh；
B) whether model is closed；
The quantity N of the topology unit includes:
C) number of vertices；
D) dough sheet number；
The bounding box information B includes:
E) length size；
F) center position；
This three parts information constitutes a 3D data block.If giving a 3D data block D, our cryptographic Hash calculates public
Formula are as follows:
WhereinIndicate step-by-step exclusive or.
In the present embodiment, before the step 100, further includes: analytical calculation is associated to 3D data block, to phase
The 3D data block of mutual correlation is integrated.
Specifically, for certain model treatment algorithms, the related 3D data block ability of incoming two panels or more is needed
It is enough calculated, such as model splicing algorithm.And there are two kinds of relationships between 3D data block:
(1) relationship in spatial domain.This is the most intuitive incidence relation of 3D data block.Assuming that the bounding box of 3D data group X
For B (X), the bounding box of data block Y is B (Y), ifThen think to deposit between 3D data block X and Y
In the association of spatial domain, need to issue Processing Algorithm together；
(2) relationship in time-domain.Since some 3D data blocks are that equipment persistently generates whithin a period of time, such as 3D
When one model of scanner scanning or SLAM camera does scene scanning building, and is limited to the objective limitation of physics, and camera is in the time
It is upper to be teleported to another point from a point, therefore continuous 3D data block is also typically present mutual pass in time-domain
Connection.Assuming that the timestamp of 3D data block X is t (X) and the timestamp of data block Y is t (Y), then as | t (X)-t (B) | < ρ, wherein
ρ is a sufficiently small time interval depending on concrete application scene, then it is assumed that there are time-domains between data block X and Y
On association, need to issue related algorithm together and process.
In summary two relationships give 3D data block X, it is assumed that and all set of data blocks are combined into K, andWe obtain
With the related data acquisition system of 3D data block X are as follows:
KX={ P1, P2..., Pn, Pi∈ K, | t (X)-t (Y) | < ρ and
The interrelated relationship between each 3D data block is obtained based on association analysis method, and to the 3D data that are mutually related
Block is integrated, and the 3D data block that will be mutually related in subsequent processing is handled together.
Step 110,3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block.
In the present embodiment, the step 110 includes that 3D data block is assigned to by the cryptographic Hash based on the 3D data block
On corresponding computer.
Specifically, the calculate node in distributed system (i.e. each computer) equably occupies a segment value on Hash ring
Space, it is empty that the cryptographic Hash that 3D data block obtains after Hash calculation belongs to the value which calculate node is occupied on Hash ring
Between, then the 3D data block may be assigned to calculate node processing.Hash algorithm can guarantee to receive in mathematical theory
Data are evenly distributed on Hash ring, to distribute to different calculate nodes, achieve the purpose that load balancing.
Cryptographic Hash, i.e. HASH value are by carrying out one group of binary value that cryptographic calculation obtains, main use to file content
Way is for file verification or signature.The cryptographic Hash that different files (even subtle difference) obtains is all different, therefore is breathed out
Uncommon value can be used as the differentiation of file uniqueness.For 3D data block, the cryptographic Hash of each 3D data block is different from, therefore can be incited somebody to action
Cryptographic Hash and 3D data block correspond, to carry out the distribution of calculate node to 3D data block.
Step 120, the 3D data block is handled in calculate node to obtain at least one the first 3D data group.
In the present embodiment, the step 120 include 3D data block is merged, is spliced, three-dimensional reconstruction, grid it is raw
At, optimization one of or it is a variety of operation to obtain at least one the first 3D data group.For example, in calculate node, to two pieces
Consecutive points cloud/grid 3D data block is spliced to obtain a 3D data group.
It is appreciated that the 3D data block in calculate node is integrated to obtain by aforementioned association analysis calculation method,
Therefore interrelated between 3D data block, the step 120 is based on the relationship that is mutually related and handles 3D data block.
Step 130, it merges the first 3D data group to obtain the 2nd 3D data group.
In the present embodiment, described that the 3D data block is handled in calculate node to obtain at least one the first 3D
The first 3D data group is merged to obtain the 2nd 3D data group to be to realize based on MapReduce model by data group with described
's.
MapReduce is a kind of programming model, the concurrent operation for large-scale dataset (being greater than 1TB).Concept " Map
(mapping) " and " Reduce (reduction) ", are their main thoughts, are borrowed in Functional Programming, there are also from
The characteristic borrowed in vector programming language.It greatly facilitate programming personnel will not distributed parallel programming in the case where,
The program of oneself is operated in distributed system.Current software realization is to specify Map (mapping) function, is used to one
Group key-value pair is mapped to one group of new key-value pair, concurrent Reduce (reduction) function is specified, for guaranteeing the key of all mappings
Value to each of share identical key group.
In brief, a Map function is exactly each element of notional list to some independent elements composition
Carry out specified operation.In fact, each element is independently operated, and original list has not changed as, because here
A new list is created to save new answer.This is to say, Map operation can be with highly-parallel, this is to high-performance
It is required that application and parallel computation field demand it is highly useful.
And Reduce operation refers to carrying out merging appropriate according to correlation degree to the element of a list.
MapReduce can be interpreted as, the rambling data of a pile are summed up according to certain feature, are then located
It manages and obtains result to the end.What Map was faced is rambling irrelevant data, it parses each data, Cong Zhongti
Key and value is taken out, that is, is extracted the feature of data.After the Shuffle stage of MapReduce, in Reduce
What the stage was seen is all the data concluded, we can do further processing to be tied on this basis
Fruit.
Specifically, the application of MapReduce model in the present invention is then first with Map function to phase in each calculate node
The 3D data block of mutual correlation carries out parallel processing and respectively obtains the first 3D data group, then with Reduce function to the first 3D data
Group merges, and the first 3D data group that will be mutually related, which merges, becomes the 2nd 3D data group.
Step 140, by the 2nd 3D data group storage into Hadoop distributed file system.
In the present embodiment, the Hadoop distributed file system is used for according to the size of the 2nd 3D data group point
Corresponding space size and redundancy backup quantity are not set and store the 2nd 3D data group.
Specifically, the Hadoop distributed file system is obtained by deep learning training, i.e., with 3D data group
As input, by way of deep learning, the training Hadoop distributed file system is according to the 2nd 3D data group
Size be respectively set corresponding space size and redundancy backup quantity, and constantly carries out algorithm correction for output result and instructs
Practice, obtains being respectively set corresponding space size and redundancy backup quantity according to the size of the 2nd 3D data group
Hadoop distributed file system.
Hadoop distributed file system (HDFS) is configured to be suitble to operate in common hardware (commodity
Hardware the distributed file system on).It and existing distributed file system have many common ground.But meanwhile it and
The difference of other distributed file systems is also apparent.HDFS is the system of an Error Tolerance.HDFS can be provided
The data access of high-throughput, the application being very suitable on large-scale dataset.HDFS relaxes a part of POSIX constraint, comes
Realize that streaming reads the purpose of file system data.
It is above-mentioned that the cryptographic Hash of 3D data block is first calculated according to processing method, further according to 3D data block cryptographic Hash by 3D
Data block is assigned to corresponding calculate node, is merged by processing associated 3D data block merging into 3D data group, and deposited
It stores up and is handled in Hadoop distributed file system, substantially increase the processing speed and treatment effeciency to 3D data block,
Avoid the excessive problem of computer load.
It should be understood that although each step in the flow chart of Fig. 1 is successively shown according to the instruction of arrow, this
A little steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly state otherwise herein, these steps
It executes there is no the limitation of stringent sequence, these steps can execute in other order.Moreover, at least part in Fig. 1
Step may include that perhaps these sub-steps of multiple stages or stage are executed in synchronization to multiple sub-steps
It completes, but can execute at different times, the execution sequence in these sub-steps or stage, which is also not necessarily, successively to be carried out,
But it can be executed in turn or alternately at least part of the sub-step or stage of other steps or other steps.
In one embodiment, as shown in Fig. 2, providing a kind of processing unit, comprising: cryptographic Hash computing module 200 divides
With module 210, mapping value computing module 220, merging module 230 and memory module 240, in which:
The cryptographic Hash of 3D data block is calculated for the data information based on 3D data block for cryptographic Hash computing module 200；
3D data block is assigned to corresponding calculating for the cryptographic Hash based on the 3D data block and saved by distribution module 210
Point；
Mapping value computing module 220, in calculate node to the 3D data block handled to obtain at least one first
3D data group；
Merging module 230 merges the first 3D data group to obtain the 2nd 3D data group；
Memory module 240, for storing the 2nd 3D data group into Hadoop distributed file system.
Specific about 3D data processing equipment limits the restriction that may refer to above for 3D data processing method,
This is repeated no more.Modules in above-mentioned 3D data processing equipment can come fully or partially through software, hardware and combinations thereof
It realizes.Above-mentioned each module can be embedded in the form of hardware or independently of in the processor in computer equipment, can also be with software
Form is stored in the memory in computer equipment, executes the corresponding operation of the above modules in order to which processor calls.
In one embodiment, a kind of computer equipment is provided, which can be terminal, internal structure
Figure can be as shown in Figure 3.The computer equipment includes processor, the memory, network interface, display connected by system bus
Screen and input unit.Wherein, the processor of the computer equipment is for providing calculating and control ability.The computer equipment is deposited
Reservoir includes non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system and computer journey
Sequence.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The network interface of machine equipment is used to communicate with external terminal by network connection.When the computer program is executed by processor with
Realize a kind of 3D data processing method.The display screen of the computer equipment can be liquid crystal display or electric ink is shown
Screen, the input unit of the computer equipment can be the touch layer covered on display screen, be also possible on computer equipment shell
Key, trace ball or the Trackpad of setting can also be external keyboard, Trackpad or mouse etc..
It will be understood by those skilled in the art that structure shown in Fig. 3, only part relevant to application scheme is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment, including memory and processor are provided, is stored in memory
Computer program, the processor perform the steps of when executing computer program
The cryptographic Hash of 3D data block is calculated in data information based on 3D data block；
3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block；
The 3D data block is handled in calculate node to obtain at least one the first 3D data group；
It merges the first 3D data group to obtain the 2nd 3D data group；
By the 2nd 3D data group storage into Hadoop distributed file system.
In one embodiment, it is also performed the steps of when processor executes computer program
Types of models, vertex quantity, dough sheet quantity, open state, length, width and height size, center point based on 3D data block
At least one of set the cryptographic Hash that 3D data block is calculated in data information.
In one embodiment, it is also performed the steps of when processor executes computer program
3D data block is assigned on corresponding computer by the cryptographic Hash based on the 3D data block.
In one embodiment, it is also performed the steps of when processor executes computer program
3D data block is merged, is spliced, three-dimensional reconstruction, grid generate, optimization at least one of operation to obtain
At least one the first 3D data group.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of when being executed by processor
The cryptographic Hash of 3D data block is calculated in data information based on 3D data block；
3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block；
The 3D data block is handled in calculate node to obtain at least one the first 3D data group；
It merges the first 3D data group to obtain the 2nd 3D data group；
By the 2nd 3D data group storage into Hadoop distributed file system.
In one embodiment, it is also performed the steps of when computer program is executed by processor
Types of models, vertex quantity, dough sheet quantity, open state, length, width and height size, center point based on 3D data block
At least one of set the cryptographic Hash that 3D data block is calculated in data information.
In one embodiment, it is also performed the steps of when computer program is executed by processor
3D data block is assigned on corresponding computer by the cryptographic Hash based on the 3D data block.
In one embodiment, it is also performed the steps of when computer program is executed by processor
3D data block is merged, is spliced, three-dimensional reconstruction, grid generate, optimization at least one of operation to obtain
At least one the first 3D data group.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. a kind of 3D data processing method, which is characterized in that the described method includes:
The cryptographic Hash of 3D data block is calculated in data information based on 3D data block；
3D data block is assigned to corresponding calculate node by the cryptographic Hash based on the 3D data block；
The 3D data block is handled in calculate node to obtain at least one the first 3D data group；
It merges the first 3D data group to obtain the 2nd 3D data group；
By the 2nd 3D data group storage into Hadoop distributed file system.
2. the method according to claim 1, wherein 3D is calculated in the data information based on 3D data block
The cryptographic Hash of data block includes:
In types of models, vertex quantity, dough sheet quantity, open state, length, width and height size, center position based on 3D data block
At least one data information the cryptographic Hash of 3D data block is calculated.
3. the method according to claim 1, wherein the cryptographic Hash based on the 3D data block is by 3D data
Block is assigned to corresponding calculate node
3D data block is assigned on corresponding computer by the cryptographic Hash based on the 3D data block.
4. the method according to claim 1, wherein it is described in calculate node to the 3D data block at
Reason obtains at least one the first 3D data group and includes:
3D data block is merged, is spliced, three-dimensional reconstruction, grid generate, optimization at least one of operation to obtain at least
One the first 3D data group.
5. the method according to claim 1, wherein the Hadoop distributed file system is used for according to
The size of 2nd 3D data group is respectively set corresponding space size and redundancy backup quantity and stores the 2nd 3D data group.
6. the method according to claim 1, wherein being calculated in the data information based on 3D data block
Before the cryptographic Hash of 3D data block, further includes:
Analytical calculation is associated to 3D data block, 3D data block is integrated to being mutually related.
7. the method according to claim 1, wherein it is described in calculate node to the 3D data block at
Reason obtain at least one the first 3D data group with it is described merge the first 3D data group to obtain the 2nd 3D data group be
It is realized based on MapReduce model.
8. a kind of 3D data processing equipment, which is characterized in that described device includes:
The cryptographic Hash of 3D data block is calculated for the data information based on 3D data block for cryptographic Hash computing module；
3D data block is assigned to corresponding calculate node for the cryptographic Hash based on the 3D data block by distribution module；
Mapping value computing module handles the 3D data block in calculate node to obtain at least one the first 3D data
Group；
Merging module merges the first 3D data group to obtain the 2nd 3D data group；
Memory module, for storing the 2nd 3D data group into Hadoop distributed file system.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In the step of processor realizes any one of claims 1 to 7 the method when executing the computer program.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claims 1 to 7 is realized when being executed by processor.
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| CN113297400A (en) * | 2021-05-31 | 2021-08-24 | 西北工业大学 | Metadata extraction method of 3D printing model |
| CN113297400B (en) * | 2021-05-31 | 2024-04-30 | 西北工业大学 | Metadata extraction method of 3D printing model |
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