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Scalable Human-Machine Point Cloud Compression

Mateen Ulhaq, Ivan V. Bajić

TL;DR

The paper tackles the challenge of running machine vision on edge devices by introducing a scalable point-cloud codec that supports a base task (classification) and an enhancement path for human-viewable reconstruction. It builds on PointNet++ and uses latent-space splitting to create a base $\\hat{y}_1$ for classification and an enhancement $\\hat{y}_2$ for reconstruction, enabling bitrate scaling and robustness to network conditions. The model is trained with a joint rate-distortion objective, and experiments on ModelNet40 show substantial improvements in base-task accuracy over prior non-specialized codecs while maintaining competitive reconstruction performance at low bitrates. This scalable approach offers practical benefits for edge-to-cloud pipelines and can be extended to additional tasks and datasets in future work.

Abstract

Due to the limited computational capabilities of edge devices, deep learning inference can be quite expensive. One remedy is to compress and transmit point cloud data over the network for server-side processing. Unfortunately, this approach can be sensitive to network factors, including available bitrate. Luckily, the bitrate requirements can be reduced without sacrificing inference accuracy by using a machine task-specialized codec. In this paper, we present a scalable codec for point-cloud data that is specialized for the machine task of classification, while also providing a mechanism for human viewing. In the proposed scalable codec, the "base" bitstream supports the machine task, and an "enhancement" bitstream may be used for better input reconstruction performance for human viewing. We base our architecture on PointNet++, and test its efficacy on the ModelNet40 dataset. We show significant improvements over prior non-specialized codecs.

Scalable Human-Machine Point Cloud Compression

TL;DR

The paper tackles the challenge of running machine vision on edge devices by introducing a scalable point-cloud codec that supports a base task (classification) and an enhancement path for human-viewable reconstruction. It builds on PointNet++ and uses latent-space splitting to create a base for classification and an enhancement for reconstruction, enabling bitrate scaling and robustness to network conditions. The model is trained with a joint rate-distortion objective, and experiments on ModelNet40 show substantial improvements in base-task accuracy over prior non-specialized codecs while maintaining competitive reconstruction performance at low bitrates. This scalable approach offers practical benefits for edge-to-cloud pipelines and can be extended to additional tasks and datasets in future work.

Abstract

Due to the limited computational capabilities of edge devices, deep learning inference can be quite expensive. One remedy is to compress and transmit point cloud data over the network for server-side processing. Unfortunately, this approach can be sensitive to network factors, including available bitrate. Luckily, the bitrate requirements can be reduced without sacrificing inference accuracy by using a machine task-specialized codec. In this paper, we present a scalable codec for point-cloud data that is specialized for the machine task of classification, while also providing a mechanism for human viewing. In the proposed scalable codec, the "base" bitstream supports the machine task, and an "enhancement" bitstream may be used for better input reconstruction performance for human viewing. We base our architecture on PointNet++, and test its efficacy on the ModelNet40 dataset. We show significant improvements over prior non-specialized codecs.
Paper Structure (12 sections, 2 equations, 4 figures, 1 table)

This paper contains 12 sections, 2 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. Proposed codec
  4. Preliminaries
  5. Scalable human-machine compression codec
  6. Proposed architecture
  7. Downsampling
  8. Feature compression
  9. Upsampling
  10. Experiments
  11. Results
  12. Conclusion

Figures (4)

  • Figure 1: High-level comparison of codec architectures.
  • Figure 2: Proposed codec architecture.
  • Figure 3: Proposed codec architecture (details).
  • Figure 4: Rate-accuracy (RA) and rate-distortion (RD) curves on the ModelNet40 dataset, with rate units of bits per point (bpp) scaled for 1024 points.