metadata
license: bsd-3-clause-clear
[CVPR 2025] GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection
This repository features checkpoints for GBlobs, a parameter-free method designed to explicitly capture the local structure within point clouds. GBlobs lead to substantial improvements in domain generalization and cross-domain performance.
Abstract
LiDAR-based 3D detectors need large datasets for training, yet they struggle to generalize to novel domains. Domain Generalization (DG) aims to mitigate this by training detectors that are invariant to such domain shifts. Current DG approaches exclusively rely on global geometric features (point cloud Cartesian coordinates) as input features. Over-reliance on these global geometric features can, however, cause 3D detectors to prioritize object location and absolute position, resulting in poor cross-domain performance. To mitigate this, we propose to exploit explicit local point cloud structure for DG, in particular by encoding point cloud neighborhoods with Gaussian blobs, GBlobs. Our proposed formulation is highly efficient and requires no additional parameters. Without any bells and whistles, simply by integrating GBlobs in existing detectors, we beat the current state-of-the-art in challenging single-source DG benchmarks by over 21 mAP (Waymo->KITTI), 13 mAP (KITTI->Waymo), and 12 mAP (nuScenes->KITTI), without sacrificing in-domain performance. Additionally, GBlobs demonstrate exceptional performance in multi-source DG, surpassing the current state-of-the-art by 17, 12, and 5 mAP on Waymo, KITTI, and ONCE, respectively.
Citation
If you find our work useful, please consider citing us:
@inproceedings{cvpr2025gblobs,
title={{GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection}},
author={Du\v{s}an Mali\'c and Christian Fruhwirth-Reisinger and Samuel Schulter and Horst Possegger},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2025}
}