CN116095181A - Point cloud compression storage method and device based on object storage - Google Patents
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Abstract
The invention discloses a point cloud compression storage method and device based on object storage, and relates to the technical field of emerging information. Aiming at the technical problems that a large amount of space is consumed during point cloud data storage, transmission is not facilitated, and collected mass data cannot be stored efficiently and stably, the invention provides a point cloud compression storage method based on object storage, which provides a point cloud compression strategy of object and bucket levels, divides, compresses and merges cloud data, stores the cloud data into a cache, aggregates the cloud data in the cache and transmits the cloud data into a storage engine, reduces multiple input and output to one input and output, improves writing performance of object storage, records index information and position information of the data, provides support for subsequent data reading, and realizes efficient and stable data transmission and storage.
Description
Technical Field
The invention relates to the technical field of emerging information, in particular to a point cloud compression storage method and device based on object storage.
Background
The point cloud data is one of the most important three-dimensional data expression modes, and is widely applied to the fields of smart cities, automatic driving, metauniverse and the like.
The original point cloud is stored in a format composed of 3D coordinates and an attribute list, and the data volume is very large. In the field of autopilot, more and more sensors such as lidar, cameras and the like are deployed on vehicles, which can generate data of approximately 1GB per second, and only testing vehicles usually generates data of 20 to 40TB per day, high-performance storage support is necessarily required to prevent data loss. The object storage is used for large-scale high-concurrency storage of massive unstructured data, and is an optimal storage scheme of point cloud data. In Ceph, the object store service provides a high availability store service to the outside through multiple object store gateways RGW, and the store backend uses bluestore as the store engine to store data. When a gateway is accessed by a large number of requests, the storage engine service avalanche caused by resource exhaustion due to the large number of requests needs to be avoided. Because the point cloud is a set of mass points, the storage point cloud not only consumes a large amount of space, but also is unfavorable for transmission, the existing object storage technology faces a great challenge in a mass point cloud data storage scene, and mass data collected in scenes such as automatic driving, metauniverse and the like cannot be stored efficiently and stably.
The storage method suitable for the point cloud data is found by combining the characteristics of the point cloud data, and the point cloud data with the lowest cost can be stored in the limited I/O transmission bandwidth.
Disclosure of Invention
Aiming at the technical problems that a large amount of space is consumed during point cloud data storage, transmission is not facilitated, and collected mass data cannot be efficiently and stably stored, the invention provides a point cloud compression storage method based on object storage, which is characterized by comprising the following steps:
step S0: acquiring point cloud data;
step S1: and (3) processing point cloud data: dividing the point cloud data into data blocks, slicing the data blocks, and dividing the data blocks into data slices;
step S2: setting compression parameters: processing and combining the data slices obtained in the step S1 according to the set compression parameters to obtain compressed data blocks;
step S3: compressed data block aggregation: and (3) storing the compressed data blocks obtained in the step (S3) into a cache, and after all the compressed data blocks are aggregated in the cache, transmitting the aggregated compressed data blocks into a storage engine.
Further, the cloud data structure in step S0 includes location information and attribute information.
Further, the data block in the step S1 is obtained by the following method:
if the size of the point cloud data is smaller than the uploading threshold value, the point cloud data is used as a data block,
and if the size of the point cloud data is larger than the uploading threshold value, performing blocking processing on the point cloud data, and dividing the point cloud data into a plurality of data blocks.
Further, the compression parameters in the step S2 include a compression switch, a compression algorithm, and compression attributes, where the compression attributes include bucket compression and object compression, and the compression algorithm includes a first video-based point cloud compression V-PCC and a geometric-based point cloud compression G-PCC.
Further, when the compression switch is off, the data slices are directly combined and then used as compressed data blocks, when the compression switch is on, the compression processing is carried out according to the compression attribute,
if the compression attribute is barrel compression, compressing the object data blocks belonging to the barrel to obtain compressed data blocks after compression,
and if the compression object is object compression, compressing the object data block, and obtaining a compressed data block after compression.
Further, the compressed data pieces in step S2 are combined into compressed data blocks according to the specified size.
Further, in the cloud data processing process, index information, position information and compression information of each data block and each data slice are recorded, and a mapping relation is established.
Further, according to the index information, the position information and the compression information of each data block and each data sheet, the data blocks are extracted from the storage engine, the data sheets are extracted from the data blocks, and the original data are obtained after decompression and combination, so that the data downloading is realized.
Further, the data download is divided into an overall download and a range download,
the whole downloading is to read the data block from the storage engine according to the index information of the data block, obtain the data piece from the data block according to the position information of the data piece, store the data piece into a cache, call a decompression plug-in for decompression according to the compression information, obtain the decompressed data piece, and combine the data pieces to obtain the original data;
the range downloading is to obtain the index information of the data block and the position information of the data sheet according to the appointed downloaded byte range, and then obtain the original data according to the whole downloading process.
A point cloud compression storage system based on object storage, which uses the point cloud compression storage method based on object storage as claimed in any one of the above, and is characterized by comprising the following modules:
the point cloud data processing system comprises: acquiring point cloud data, dividing the point cloud data into data blocks, slicing the data blocks into data slices, and setting compression parameters;
and the point cloud data compression and decompression system comprises the following components: acquiring a data sheet and compression parameters output by a point cloud data processing system, and processing and combining the compression parameters of the data sheet to obtain a compressed data block;
storage engine: and acquiring compressed data blocks output by the point cloud data compression system, storing the compressed data blocks into a cache, and after all the compressed data blocks are aggregated in the cache, transmitting the compressed data blocks into a storage engine.
Compared with the prior art, the invention has the beneficial effects that:
firstly, a point cloud compression strategy of an object and bucket level is provided based on a distributed object storage system, the application range of a data storage rule of an object gateway cluster is subjected to fine granularity processing, so that the application range is applied to a specific bucket and object level, point cloud compression algorithms G-PCC and V-PCC are introduced for point cloud data compression, and a point cloud data storage scene is better adapted.
And secondly, caching and merging the compressed data blocks, and then transmitting the data blocks into a storage engine, so that multiple input and output are reduced to one input and output, and the writing performance of object storage is improved.
Thirdly, the point cloud data can be smoothly loaded in the memory by dividing the point cloud data, and the index of the obtained data block can be used for data aggregation. The point cloud compression algorithms G-PCC and V-PCC introduced in the object gateway RGW can meet the full scene compression of dense point clouds and sparse point clouds, and the input and output transmission bandwidth and the storage space are saved.
Fourth, the data block compression ratio after the compression algorithm can reach 100 on average: and 1, after caching the compressed data blocks, merging according to the point cloud segmentation indexes, so that the waste of storage engine resources caused by excessive metadata and excessive input/output times is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a logic diagram of a system according to the present invention;
FIG. 2 is a flow chart of an atomic upload data store according to an embodiment of the present invention;
FIG. 3 is a block upload data storage flow diagram of one embodiment of the present invention;
FIG. 4 is a flow chart of point cloud compression according to an embodiment of the present invention;
fig. 5 is a flow chart of point cloud decompression according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be appreciated by those of skill in the art that the following specific embodiments or implementations are provided as a series of preferred arrangements of the present invention for further explanation of the specific disclosure, and that the arrangements may be used in conjunction or association with each other, unless it is specifically contemplated that some or some of the specific embodiments or implementations may not be associated or used with other embodiments or implementations. Meanwhile, the following specific examples or embodiments are merely provided as an optimized arrangement, and are not to be construed as limiting the scope of the present invention.
The following describes specific embodiments of the present invention with reference to the drawings (tables).
Detailed description of the preferred embodiments
Aiming at the technical problems that a large amount of space is consumed during point cloud data storage, transmission is not facilitated, and collected mass data cannot be stored efficiently and stably, the invention provides a point cloud compression storage method based on object storage, which provides an object and bucket-level point cloud compression strategy, divides, compresses and merges cloud data, stores the cloud data into a cache, aggregates the cloud data in the cache and transmits the cloud data into a storage engine, records index information and position information of the data, provides support for subsequent data reading, and realizes efficient and stable data transmission and storage.
The invention provides a point cloud compression storage method based on object storage, which is characterized by comprising the following steps:
step S0: and acquiring point cloud data.
The method comprises the steps of acquiring point cloud data, such as laser radar real-time scanning in an automatic driving scene to acquire point cloud, wherein a point cloud data structure comprises position information and attribute information (color and the like), and the common point cloud format is (X, Y, Z, R, G and B). The point cloud features large data size, so it can be transmitted in blocks with index information, and the blocks upload and download objects to ensure that the request will not timeout and the transmission bandwidth is limited to the existing hardware capacity
Step S1: and (3) processing point cloud data: and dividing the point cloud data into data blocks, slicing the data blocks, and dividing the data blocks into data slices.
The point cloud data block can be divided into atomic uploading and block uploading according to the size of the cut block.
If the size of the point cloud data is smaller than the atom uploading threshold value, slicing the point cloud data to obtain data slices;
if the size of the point cloud data is larger than the atom uploading threshold value, performing block processing on the point cloud data, dividing the point cloud data into a plurality of data blocks, and performing block processing on the data blocks to obtain data slices.
The atom uploading threshold value can be set to be 5GB, when the point cloud data is smaller than 5G, the point cloud data is used as a data block, the data block is divided into a plurality of data pieces, atom uploading is directly carried out, and when the point cloud data is larger than 5G, the object storage module is further divided into blocks to complete uploading, and each block is equivalent to one atom uploading.
Each data block is provided with index information, and the starting position and the ending position of the slice in the data block are recorded when the slicing processing is carried out.
Step S2: and (3) setting compression parameters, and processing and combining the data slices obtained in the step (S1) according to the set compression parameters to obtain compressed data blocks.
Compression parameters include compression switch, compression algorithm, and compression attributes including bucket compression and object compression.
The compression switch determines whether to compress the data, the compression algorithm determines which way to compress, and the compression attribute determines the compression object.
Video-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC) are compression plug-ins provided in RGWs. The two compression algorithms are two coding standards approved by MPEG in FDIS in 2020 for point cloud compression, and are used for point cloud data compression, so that the point cloud data storage scene is better adapted.
The distributed object storage provides users, storage buckets and objects, wherein the buckets are subordinate to the users, the objects are subordinate to the storage buckets, the processing system provides a radosgw-admin interface to control compression methods and compression switches of the storage buckets and the objects, and the setting method is as follows:
barrel level compression settings:
radosgw-admin compress set-compress-scope＝bucket–bucket＝bucket0625–compress_tpe＝G-PCC/V-PCC
radosgw-admin compress enable/disable–compress-scope＝bucket–bucket＝compress-02
object level compression setting:
x-amz-compress＝G-PCC/VPCC
when the compression switch is off, the data slices are directly combined and then used as compressed data blocks, when the compression switch is on, the compression processing is carried out according to the compression attribute,
if the compression attribute is barrel compression, compressing the object data blocks belonging to the barrel to obtain compressed data blocks after compression,
and if the compression object is object compression, compressing the object data block, and obtaining a compressed data block after compression.
Firstly, the compression attribute of the barrel to which the object belongs is read, if the barrel is compressed, the compression component is called to compress the data block, and the QAT chip can be used for compressing the data to be compressed during compression. If the bucket is not set for compression, it is checked whether the object itself has compression properties, and if the object is compressed, the compression component is also invoked to compress the data block. If neither compression is set, the data slice is directly transferred into the storage engine.
Step S3: compressed data block aggregation: and (3) storing the compressed data blocks obtained in the step (S2) into a cache, and after all the compressed data blocks are aggregated in the cache, transmitting the aggregated compressed data blocks into a storage engine.
In the cloud data processing process, index information, position information and compression information of each data block and each data sheet are recorded, and a mapping relation is established so as to acquire positions of the data sheets before and after compression. The position information and the object index information are stored in the object metadata, and the key metadata is cached.
The compressed data is generally small objects, and the direct input of the data into a storage engine can cause the problems of excessive metadata, waste of physical storage space, slow time and the like. The compressed data is stored in the buffer memory, and then is merged and transferred into the storage engine.
The data uploaded to the object storage system is also required to provide a downloading interface, the downloading object is divided into integral downloading and range downloading, the range downloading designates the byte range needing to be downloaded, the position information of the object data sheet is cached in the metadata, and only the integral downloading process of the corresponding information is required to be acquired. When the whole downloading is performed, the data blocks, the corresponding metadata and part of the cached metadata need to be read from the back-end storage engine, the data pieces are scattered according to the position information of the metadata cache, the scattered slice cache is carried out, and the decompression plug-in is invoked to decompress. And after the decompressed data slices are obtained, merging the data slices to obtain the original data.
FIG. 2 is a flow chart of data storage.
Collecting point cloud data such as meta-universe or smart city or automatic driving point cloud data, if the size of the point cloud data is smaller than an atom uploading threshold value, namely 5GB, uploading atoms, namely slicing the point cloud data to obtain data pieces, storing index information of the data blocks and position information of the data pieces in object metadata, and caching key metadata
Detailed description of the preferred embodiments
The invention also provides a point cloud compression storage system based on object storage, which is characterized by comprising the following modules according to any one of the specific embodiments:
the point cloud data processing system comprises: acquiring point cloud data, dividing the point cloud data into data blocks, slicing the data blocks into data slices, and setting compression parameters;
and the point cloud data compression and decompression system comprises the following components: acquiring a data sheet and compression parameters output by a point cloud data processing system, and processing and combining the compression parameters of the data sheet to obtain a compressed data block;
storage engine: and acquiring compressed data blocks output by the point cloud data compression system, storing the compressed data blocks into a cache, and after all the compressed data blocks are aggregated in the cache, transmitting the compressed data blocks into a storage engine.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The point cloud compression storage method based on object storage is characterized by comprising the following steps of:
step S0: acquiring point cloud data;
step S1: and (3) processing point cloud data: dividing the point cloud data into data blocks, slicing the data blocks, and dividing the data blocks into data slices;
step S2: setting compression parameters: processing and combining the data slices obtained in the step S1 according to the set compression parameters to obtain compressed data blocks;
step S3: compressed data block aggregation: and (3) storing the compressed data blocks obtained in the step (S3) into a cache, and after all the compressed data blocks are aggregated in the cache, transmitting the aggregated compressed data blocks into a storage engine.
2. The method according to claim 1, wherein the point cloud data structure in step S0 includes location information and attribute information.
3. The point cloud compression storage method based on object storage according to claim 1, wherein the data block in step S1 is obtained by:
if the size of the point cloud data is smaller than the uploading threshold value, the point cloud data is used as a data block,
and if the size of the point cloud data is larger than the uploading threshold value, performing blocking processing on the point cloud data, and dividing the point cloud data into a plurality of data blocks.
4. The method according to claim 1, wherein the compression parameters in step S2 include a compression switch, a compression algorithm, and compression attributes, the compression attributes include bucket compression and object compression, and the compression algorithm includes a first video-based point cloud compression V-PCC and a second geometric-based point cloud compression G-PCC.
5. The point cloud compression storage method based on object storage according to claim 1, wherein when the compression switch is off, the data pieces are directly combined and then used as compressed data blocks, when the compression switch is on, the compression processing is performed according to the compression attribute,
if the compression attribute is barrel compression, compressing the object data blocks belonging to the barrel to obtain compressed data blocks after compression,
and if the compression object is object compression, compressing the object data block, and obtaining a compressed data block after compression.
6. The method for storing point cloud data in a compressed manner according to claim 1, wherein the compressed data pieces in step S2 are combined into compressed data blocks according to a specified size.
7. The method for storing point cloud data according to claim 1, wherein index information, position information and compression information of each data block and each data slice are recorded and a mapping relation is established in the cloud data processing process.
8. The method for compressing and storing point cloud based on object storage according to claim 7, wherein according to the index information, the position information and the compression information of each data block and each data slice, the data blocks are extracted from the storage engine, the data slices are extracted from the data blocks, and the original data are obtained after decompression and merging, so that the data downloading is realized.
9. The method for point cloud compressed storage based on object storage according to claim 8, wherein said data download is divided into an overall download and a range download,
the whole downloading is to read the data block from the storage engine according to the index information of the data block, obtain the data piece from the data block according to the position information of the data piece, store the data piece into a cache, call a decompression plug-in for decompression according to the compression information, obtain the decompressed data piece, and combine the data pieces to obtain the original data;
the range downloading is to obtain the index information of the data block and the position information of the data sheet according to the appointed downloaded byte range, and then obtain the original data according to the whole downloading process.
10. A point cloud compression storage system based on object storage, which uses a point cloud compression storage method based on object storage according to any one of claims 1 to 9, and is characterized by comprising the following modules:
the point cloud data processing system comprises: acquiring point cloud data, dividing the point cloud data into data blocks, slicing the data blocks into data slices, and setting compression parameters;
and the point cloud data compression and decompression system comprises the following components: acquiring a data sheet and compression parameters output by a point cloud data processing system, and processing and combining the compression parameters of the data sheet to obtain a compressed data block;
storage engine: and acquiring compressed data blocks output by the point cloud data compression system, storing the compressed data blocks into a cache, and after all the compressed data blocks are aggregated in the cache, transmitting the compressed data blocks into a storage engine.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103886625A (en) * | 2014-01-09 | 2014-06-25 | 北京工业大学 | Point cloud data sparse representation method based on compressed sensing |
| CN104750854A (en) * | 2015-04-16 | 2015-07-01 | 武汉海达数云技术有限公司 | Mass three-dimensional laser point cloud compression storage and rapid loading and displaying method |
| CN108009979A (en) * | 2017-12-15 | 2018-05-08 | 湖北大学 | Three-dimensional point cloud compression and storage method and system based on space-time data fusion |
| CN111432210A (en) * | 2020-04-30 | 2020-07-17 | 中山大学 | Point cloud attribute compression method based on filling |
| WO2020189895A1 (en) * | 2019-03-21 | 2020-09-24 | 엘지전자 주식회사 | Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method |
| CN112581552A (en) * | 2020-12-14 | 2021-03-30 | 深圳大学 | Self-adaptive blocking point cloud compression method and device based on voxels |
| US20210192769A1 (en) * | 2019-12-19 | 2021-06-24 | Lg Electronics Inc. | Methods and devices for providing content |
| CN113486276A (en) * | 2021-08-02 | 2021-10-08 | 北京京东乾石科技有限公司 | Point cloud compression method, point cloud rendering method, point cloud compression device, point cloud rendering equipment and storage medium |
| CN113766228A (en) * | 2020-06-05 | 2021-12-07 | Oppo广东移动通信有限公司 | Point cloud compression method, encoder, decoder, and storage medium |
| CN114503164A (en) * | 2019-10-07 | 2022-05-13 | 华为技术有限公司 | Video-based point cloud compression (V-PCC) component synchronization |
| CN114631118A (en) * | 2019-10-02 | 2022-06-14 | 苹果公司 | Tailoring search spaces for nearest neighbor determination in point cloud compression |
-
2022
- 2022-12-30 CN CN202211722572.1A patent/CN116095181B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103886625A (en) * | 2014-01-09 | 2014-06-25 | 北京工业大学 | Point cloud data sparse representation method based on compressed sensing |
| CN104750854A (en) * | 2015-04-16 | 2015-07-01 | 武汉海达数云技术有限公司 | Mass three-dimensional laser point cloud compression storage and rapid loading and displaying method |
| CN108009979A (en) * | 2017-12-15 | 2018-05-08 | 湖北大学 | Three-dimensional point cloud compression and storage method and system based on space-time data fusion |
| WO2020189895A1 (en) * | 2019-03-21 | 2020-09-24 | 엘지전자 주식회사 | Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method |
| CN114631118A (en) * | 2019-10-02 | 2022-06-14 | 苹果公司 | Tailoring search spaces for nearest neighbor determination in point cloud compression |
| CN114503164A (en) * | 2019-10-07 | 2022-05-13 | 华为技术有限公司 | Video-based point cloud compression (V-PCC) component synchronization |
| US20210192769A1 (en) * | 2019-12-19 | 2021-06-24 | Lg Electronics Inc. | Methods and devices for providing content |
| CN111432210A (en) * | 2020-04-30 | 2020-07-17 | 中山大学 | Point cloud attribute compression method based on filling |
| CN113766228A (en) * | 2020-06-05 | 2021-12-07 | Oppo广东移动通信有限公司 | Point cloud compression method, encoder, decoder, and storage medium |
| CN112581552A (en) * | 2020-12-14 | 2021-03-30 | 深圳大学 | Self-adaptive blocking point cloud compression method and device based on voxels |
| CN113486276A (en) * | 2021-08-02 | 2021-10-08 | 北京京东乾石科技有限公司 | Point cloud compression method, point cloud rendering method, point cloud compression device, point cloud rendering equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| JIANQIANG WANG ET AL.: "Multiscale Point Cloud Geometry Compression", 《 ARXIV》, 7 November 2020 (2020-11-07) * |
| 张卉冉 等: ""点云压缩研究进展与趋势"", 武汉大学学报（信息科学版）》, vol. 48, no. 2, 5 March 2022 (2022-03-05), pages 193 * |
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