BUFFER-X: 多様なシーンでのゼロショット点群位置合わせに向けて
BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes
環境ごとの再学習やパラメータ調整なしで多様なシーンに適用できるゼロショット点群位置合わせ手法を提案し、11データセットで汎化性能を実証した。
著者: Minkyun Seo, Hyungtae Lim, Kanghee Lee, Luca Carlone, Jaesik Park
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
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.
