CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection
This addresses the problem of adapting 3D detection models across different LiDAR types for applications like autonomous driving, but it is incremental as it builds on existing self-training and alignment methods.
The paper tackles the problem of unsupervised domain adaptation for 3D object detection across different LiDAR sensors, which is challenging due to disparities in point densities and arrangements. It achieves state-of-the-art performance on cross-device datasets, particularly for large gaps between mechanical scanning and solid-state LiDARs.
Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on the raw data representation with disparate point densities and point arrangements. By exploring domain-invariant 3D geometric characteristics and motion patterns, we present an unsupervised domain adaptation method that overcomes above difficulties. First, we propose the Spatial Geometry Alignment module to extract similar 3D shape geometric features of the same object class to align two domains, while eliminating the effect of distinct point distributions. Second, we present Temporal Motion Alignment module to utilize motion features in sequential frames of data to match two domains. Prototypes generated from two modules are incorporated into the pseudo-label reweighting procedure and contribute to our effective self-training framework for the target domain. Extensive experiments show that our method achieves state-of-the-art performance on cross-device datasets, especially for the datasets with large gaps captured by mechanical scanning LiDARs and solid-state LiDARs in various scenes. Project homepage is at https://github.com/4DVLab/CL3D.git