Dense-SfM: Structure from Motion with Dense Consistent Matching

• URL: http://arxiv.org/abs/2501.14277v1
• Date: Fri, 24 Jan 2025 06:45:12 GMT
• Title: Dense-SfM: Structure from Motion with Dense Consistent Matching
• Authors: JongMin Lee, Sungjoo Yoo,
• Abstract summary: We present Dense-SfM, a novel framework for dense and accurate 3D reconstruction from multi-view images. Dense-SfM integrates dense matching with a Gaussian Splatting (GS) based track extension which gives more consistent, longer feature tracks. Dense-SfM offers significant improvements in accuracy and density over state-of-the-art methods.
• Score: 10.24418219366936
• License:
• Abstract: We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint matching, which traditional SfM methods often rely on, limits both accuracy and point density, especially in texture-less areas. Dense-SfM addresses this limitation by integrating dense matching with a Gaussian Splatting (GS) based track extension which gives more consistent, longer feature tracks. To further improve reconstruction accuracy, Dense-SfM is equipped with a multi-view kernelized matching module leveraging transformer and Gaussian Process architectures, for robust track refinement across multi-views. Evaluations on the ETH3D and Texture-Poor SfM datasets show that Dense-SfM offers significant improvements in accuracy and density over state-of-the-art methods.

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