Papers › UMERegRobust -- Universal Manifold Embedding Compatible Features for Robust Point...

UMERegRobust -- Universal Manifold Embedding Compatible Features for Robust Point Cloud Registration

22 Aug 2024arXiv:2408.12380archive 2025-07-28

Yuval Haitman, Amit Efraim, Joseph M. Francos

In this paper, we adopt the Universal Manifold Embedding (UME) framework for the estimation of rigid transformations and extend it, so that it can accommodate scenarios involving partial overlap and differently sampled point clouds. UME is a methodology designed for mapping observations of the same object, related by rigid transformations, into a single low-dimensional linear subspace. This process yields a transformation-invariant representation of the observations, with its matrix form representation being covariant (i.e. equivariant) with the transformation. We extend the UME framework by introducing a UME-compatible feature extraction method augmented with a unique UME contrastive loss and a sampling equalizer. These components are integrated into a comprehensive and robust registration pipeline, named UMERegRobust. We propose the RotKITTI registration benchmark, specifically tailored to evaluate registration methods for scenarios involving large rotations. UMERegRobust achieves better than state-of-the-art performance on the KITTI benchmark, especially when strict precision of (1{\deg}, 10cm) is considered (with an average gain of +9%), and notably outperform SOTA methods on the RotKITTI benchmark (with +45% gain compared the most recent SOTA method).

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Point Cloud Registration

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Point Cloud Registration RotKITTI Registration Benchmark UMERegRobust RR@(1,0.1) 73.3 #1 of 6 Archive leaderboard report
Point Cloud Registration RotKITTI Registration Benchmark UMERegRobust RR@(1.5,0.3) 81.1 #1 of 6 Archive leaderboard report

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