Semantics-Augmented Quantization-Aware Training for Point Cloud Classification
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Point cloud classification is a pivotal procedure in 3D computer vision, and its deployment in practical applications is often constrained by limited computational and memory resources. To address these issues, we introduce a Semantics-Augmented Quantization-Aware Training (SAQAT) framework designed for efficient and precise classification of point cloud data. The SAQAT framework incorporates a point importance prediction semantic module as a side output, which assists in identifying crucial points, along with a point importance evaluation algorithm (PIEA). The semantics module leverages point importance prediction to skillfully select quantization levels based on local geometric properties and semantic context. This approach reduces errors by retaining essential information. In synergy, the PIEA acts as the cornerstone, providing an additional layer of refinement to SAQAT framework. Furthermore, we integrates a loss function that mitigates classification loss, quantization error, and point importance prediction loss, thereby fostering a reliable representation of the quantized data. The SAQAT framework is designed for seamless integration with existing point cloud models, enhancing their efficiency while maintaining high levels of accuracy. Testing on benchmark datasets demonstrates that our SAQAT framework surpasses contemporary quantization methods in classification accuracy while simultaneously economizing on memory and computational resources. Given these advantages, our SAQAT framework holds enormous potential for a wide spectrum of applications within the rapidly evolving domain of 3D computer vision. Our code is released: https://github.com/h-liming/SAQAT.
Description
CCS Concepts: Computing methodologies → Object recognition
@inproceedings{10.2312:pg.20241275,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{Semantics-Augmented Quantization-Aware Training for Point Cloud Classification}},
author = {Huang, Liming and Qin, Yunchuan and Li, Ruihui and Wu, Fan and Li, Kenli},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241275}
}