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arXiv Computer Vision By Enpeng Li, Yunzhou Zhang, Zhiyao Zhang, Dexuan Lyu, Chenyu Wang, Chiyuan Cui, Cheng Cheng

GRF-Recon: Global Ray-Field Optimization for Long-Sequence Feed-forward Reconstruction

Read the original on arXiv Computer Vision →

The paper introduces GRF-Recon, a framework for stable and scalable feed-forward 3D reconstruction from long monocular image sequences. It combines coarse-to-fine trajectory alignment, lightweight geometric prior injection via LoRA adaptation, and a hybrid-weight sparse ray-field optimization to refine local point clouds while enforcing cross-frame consistency. An efficient trajectory stitching strategy with joint ray-error optimization further reduces accumulated drift, achieving competitive trajectory accuracy compared to SLAM systems while maintaining globally consistent reconstructions in large-scale scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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