Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing

2025-05-12

AI Summary

To address the high computational and memory overhead of point cloud models (e.g., Point Transformer V3) on edge devices, this paper proposes a topology-aware and gradient-guided collaborative knowledge distillation framework. Our method introduces a novel dual-path distillation mechanism: one path explicitly models point cloud topology to enhance geometric representation learning, while the other enforces feature alignment via gradient-direction constraints for robust knowledge transfer. Evaluated under pure LiDAR input across NuScenes, SemanticKITTI, and Waymo—using joint training and cross-dataset evaluation—the student model achieves a 16× reduction in parameter count and 1.9× inference speedup over the teacher. Notably, it outperforms all existing LiDAR-only distillation methods on NuScenes semantic segmentation, establishing new state-of-the-art performance.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

Abstract

Point cloud processing has gained significant attention due to its critical role in applications such as autonomous driving and 3D object recognition. However, deploying high-performance models like Point Transformer V3 in resource-constrained environments remains challenging due to their high computational and memory demands. This work introduces a novel distillation framework that leverages topology-aware representations and gradient-guided knowledge distillation to effectively transfer knowledge from a high-capacity teacher to a lightweight student model. Our approach captures the underlying geometric structures of point clouds while selectively guiding the student model's learning process through gradient-based feature alignment. Experimental results in the Nuscenes, SemanticKITTI, and Waymo datasets demonstrate that the proposed method achieves competitive performance, with an approximately 16x reduction in model size and a nearly 1.9x decrease in inference time compared to its teacher model. Notably, on NuScenes, our method achieves state-of-the-art performance among knowledge distillation techniques trained solely on LiDAR data, surpassing prior knowledge distillation baselines in segmentation performance. Our implementation is available publicly at: https://github.com/HySonLab/PointDistill

Problem

• Enables efficient point cloud processing in resource-limited settings
• Reduces model size and inference time while maintaining performance
• Improves knowledge distillation for 3D LiDAR data segmentation

Innovation

• Topology-aware representations for knowledge distillation
• Gradient-guided feature alignment in distillation
• Lightweight model with reduced size and time