arXiv Computer Vision
Sep 22

Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation

The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.

By Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Pascal Fua
arXiv AI
Oct 16

Motion- and Gravity-Aware Feedforward 3D Reconstruction

The paper presents a motion- and gravity-aware framework for feed-forward transformer models in multi-view 3D reconstruction. By providing residual optical flow and gravity direction as privileged signals during training, the model learns stronger geometric representations while remaining efficient at inference. Experiments demonstrate consistent accuracy gains, particularly in dynamic scenes and challenging camera motions, with the optimal integration stage depending on the backbone architecture.

By Shiyao Qian, Yuan Ren, Dongfeng Bai