most citedGenS: Generalizable Neural Surface Reconstruction from Multi-View Images

5 citations · 6 across the 5 of their papers we have counts for

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cs.CV2026

Glob3R: Global Structure-from-Motion with 3D Foundation Models

Junyuan Deng, Heng Li, Kejie Qiu +7

Recent 3D geometric foundation models, such as VGGT, provide robust feed-forward 3D reconstruction by directly predicting camera poses and 3D scene points from input images. Howeve…

cs.CV2026

Towards Consistent Video Geometry Estimation

Zhu Yu, Jingnan Gao, Runmin Zhang +9

This work presents ViGeo, a feed-forward foundation model for recovering spatially dense and temporally consistent geometry from video sequences. Built upon a plain transformer arc…

cs.CV2024

Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction

Rui Peng, Shihe Shen, Kaiqiang Xiong +4

Reconstructing the high-fidelity surface from multi-view images, especially sparse images, is a critical and practical task that has attracted widespread attention in recent years.…

cs.CV20245 cited

GenS: Generalizable Neural Surface Reconstruction from Multi-View Images

Rui Peng, Xiaodong Gu, Luyang Tang +3

Combining the signed distance function (SDF) and differentiable volume rendering has emerged as a powerful paradigm for surface reconstruction from multi-view images without 3D sup…

cs.CV20241 cited

VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model

Qi Zuo, Xiaodong Gu, Lingteng Qiu +8

Generating multi-view images based on text or single-image prompts is a critical capability for the creation of 3D content. Two fundamental questions on this topic are what data we…