Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications
Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured r...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10829598/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841550795199217664 |
|---|---|
| author | Damjan Strnad Stefan Kohek Borut Zalik Libor Vasa Andrej Nerat |
| author_facet | Damjan Strnad Stefan Kohek Borut Zalik Libor Vasa Andrej Nerat |
| author_sort | Damjan Strnad |
| collection | DOAJ |
| description | Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured representation such as a voxel grid. Storing, transmitting and consuming voxelized geometry, however, remains a challenging problem for machine learning pipelines running on devices with limited amount of on-chip memory with low access latency. A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. Such on-demand expansion must be fast in order to avoid introducing substantial additional delay to the pipeline. This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. The method employs multi-level context-aware prediction of voxel occupancy based on the extracted binary feature prediction table, and encodes the residual grid with a pointerless sparse voxel octree (PSVO). We particularly focused on encoding the datasets of voxelized trees, obtained from both synthetic tree models and LiDAR point clouds of real trees. The method achieved 15.6% and 12.8% reduction of storage size with respect to plain PSVO on synthetic and real dataset, respectively. We also tested the method on a general set of diverse voxelized objects, where an average 11% improvement of storage space was achieved. |
| format | Article |
| id | doaj-art-be0f1af5c9a04696b52f9e26d2c6962a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-be0f1af5c9a04696b52f9e26d2c6962a2025-01-10T00:01:45ZengIEEEIEEE Access2169-35362025-01-01135551556110.1109/ACCESS.2025.352620210829598Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based ApplicationsDamjan Strnad0https://orcid.org/0000-0003-4468-0290Stefan Kohek1https://orcid.org/0000-0002-6210-0889Borut Zalik2https://orcid.org/0000-0003-4372-5020Libor Vasa3https://orcid.org/0000-0002-0213-3769Andrej Nerat4https://orcid.org/0000-0003-1559-9776Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaFaculty of Applied Sciences, University of West Bohemia, Pilsen, Czech RepublicFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaPoint clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured representation such as a voxel grid. Storing, transmitting and consuming voxelized geometry, however, remains a challenging problem for machine learning pipelines running on devices with limited amount of on-chip memory with low access latency. A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. Such on-demand expansion must be fast in order to avoid introducing substantial additional delay to the pipeline. This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. The method employs multi-level context-aware prediction of voxel occupancy based on the extracted binary feature prediction table, and encodes the residual grid with a pointerless sparse voxel octree (PSVO). We particularly focused on encoding the datasets of voxelized trees, obtained from both synthetic tree models and LiDAR point clouds of real trees. The method achieved 15.6% and 12.8% reduction of storage size with respect to plain PSVO on synthetic and real dataset, respectively. We also tested the method on a general set of diverse voxelized objects, where an average 11% improvement of storage space was achieved.https://ieeexplore.ieee.org/document/10829598/Voxel gridfeature predictiontree modelsprediction-based encodingkey voxelsresiduals |
| spellingShingle | Damjan Strnad Stefan Kohek Borut Zalik Libor Vasa Andrej Nerat Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications IEEE Access Voxel grid feature prediction tree models prediction-based encoding key voxels residuals |
| title | Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications |
| title_full | Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications |
| title_fullStr | Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications |
| title_full_unstemmed | Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications |
| title_short | Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications |
| title_sort | efficient encoding and decoding of voxelized models for machine learning based applications |
| topic | Voxel grid feature prediction tree models prediction-based encoding key voxels residuals |
| url | https://ieeexplore.ieee.org/document/10829598/ |
| work_keys_str_mv | AT damjanstrnad efficientencodinganddecodingofvoxelizedmodelsformachinelearningbasedapplications AT stefankohek efficientencodinganddecodingofvoxelizedmodelsformachinelearningbasedapplications AT borutzalik efficientencodinganddecodingofvoxelizedmodelsformachinelearningbasedapplications AT liborvasa efficientencodinganddecodingofvoxelizedmodelsformachinelearningbasedapplications AT andrejnerat efficientencodinganddecodingofvoxelizedmodelsformachinelearningbasedapplications |