BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
Recent advances in deep learning-based point cloud registration have improved
generalization, yet most methods still require retraining or manual parameter
tuning for each new environment. In this paper, we identify three key factors
limiting generalization: (a) reliance on environment-specific voxel size and
search radius, (b) poor out-of-domain robustness of learning-based keypoint
detectors, and (c) raw coordinate usage, which exacerbates scale discrepancies.
To address these issues, we present a zero-shot registration pipeline called
BUFFER-X by (a) adaptively determining voxel size/search radii, (b) using
farthest point sampling to bypass learned detectors, and (c) leveraging
patch-wise scale normalization for consistent coordinate bounds. In particular,
we present a multi-scale patch-based descriptor generation and a hierarchical
inlier search across scales to improve robustness in diverse scenes. We also
propose a novel generalizability benchmark using 11 datasets that cover various
indoor/outdoor scenarios and sensor modalities, demonstrating that BUFFER-X
achieves substantial generalization without prior information or manual
parameter tuning for the test datasets. Our code is available at
https://github.com/MIT-SPARK/BUFFER-X.
