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Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation
Computing accurate geometry from multi-view images is a fundamental problem in computer vision. Recent feed-forward (FF) models jointly estimate 3D geometry and camera parameters, but they typically suffer from geometry distortion caused by reconstruction ambiguity, even when ground-truth camera parameters are supplied. In this paper, we study the multi-view stereo (MVS) problem with known camera parameters and propose a novel approach that bridges conventional MVS and FF methods. Rather than casting MVS as a sequence-to-one mapping that predicts depth only for a single reference view, we refo
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T16:28:26.000Z
First collected: 2026-09-23T06:11:12.848Z. This is not the publication date.