GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds

TL;DR

GPA-3D introduces a geometry-aware prototype alignment method for unsupervised domain adaptation in 3D LiDAR object detection, reducing feature discrepancy by leveraging geometric relationships to improve cross-domain performance.

Contribution

The paper proposes a novel GPA-3D framework that explicitly uses geometric relationships and learnable prototypes to align features across domains, enhancing 3D detection generalization.

Findings

Outperforms state-of-the-art methods on Waymo, nuScenes, and KITTI benchmarks.

Effectively reduces feature distribution discrepancy across domains.

Demonstrates improved detection accuracy in unseen environments.

Abstract

LiDAR-based 3D detection has made great progress in recent years. However, the performance of 3D detectors is considerably limited when deployed in unseen environments, owing to the severe domain gap problem. Existing domain adaptive 3D detection methods do not adequately consider the problem of the distributional discrepancy in feature space, thereby hindering generalization of detectors across domains. In this work, we propose a novel unsupervised domain adaptive \textbf{3D} detection framework, namely \textbf{G}eometry-aware \textbf{P}rototype \textbf{A}lignment (\textbf{GPA-3D}), which explicitly leverages the intrinsic geometric relationship from point cloud objects to reduce the feature discrepancy, thus facilitating cross-domain transferring. Specifically, GPA-3D assigns a series of tailored and learnable prototypes to point cloud objects with distinct geometric structures. Each prototype aligns BEV (bird's-eye-view) features derived from corresponding point cloud objects on source and target domains, reducing the distributional discrepancy and achieving better adaptation. The evaluation results obtained on various benchmarks, including Waymo, nuScenes and KITTI, demonstrate the superiority of our GPA-3D over the state-of-the-art approaches for different adaptation scenarios. The MindSpore version code will be publicly available at \url{https://github.com/Liz66666/GPA3D}.