Surfel-based 3D Registration with Equivariant SE(3) Features

• URL: http://arxiv.org/abs/2508.20789v1
• Date: Thu, 28 Aug 2025 13:53:44 GMT
• Title: Surfel-based 3D Registration with Equivariant SE(3) Features
• Authors: Xueyang Kang, Hang Zhao, Kourosh Khoshelham, Patrick Vandewalle,
• Abstract summary: Point cloud registration is crucial for ensuring 3D alignment consistency of multiple local point clouds in 3D reconstruction for remote sensing or digital heritage.<n>We propose a novel surfel-based pose learning regression approach to address these issues.<n>Our method can initialize surfels from Lidar point cloud using virtual perspective camera parameters, and learns explicit $mathbfSE(3)$ equivariant features.
• Score: 34.796697445601914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud registration is crucial for ensuring 3D alignment consistency of multiple local point clouds in 3D reconstruction for remote sensing or digital heritage. While various point cloud-based registration methods exist, both non-learning and learning-based, they ignore point orientations and point uncertainties, making the model susceptible to noisy input and aggressive rotations of the input point cloud like orthogonal transformation; thus, it necessitates extensive training point clouds with transformation augmentations. To address these issues, we propose a novel surfel-based pose learning regression approach. Our method can initialize surfels from Lidar point cloud using virtual perspective camera parameters, and learns explicit $\mathbf{SE(3)}$ equivariant features, including both position and rotation through $\mathbf{SE(3)}$ equivariant convolutional kernels to predict relative transformation between source and target scans. The model comprises an equivariant convolutional encoder, a cross-attention mechanism for similarity computation, a fully-connected decoder, and a non-linear Huber loss. Experimental results on indoor and outdoor datasets demonstrate our model superiority and robust performance on real point-cloud scans compared to state-of-the-art methods.

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