← 研踪 PaperTrailarXiv:2611.12875 · v1
Click3R: Interactive Stereo 3D Reconstruction with Sparse Correspondence Clicks
Click3R：基于稀疏对应点击的交互式立体三维重建
首次提交 2026-11-10 02:58
版本更新 2026-11-10 02:58
雷达观测 2026-11-10 15:22
摘要
Despite impressive progress, existing feed-forward large 3D reconstruction models produce a single, irrevocable prediction. When these models fail, there is no mechanism to correct their mistakes. To address this limitation, we introduce Click3R, a framework for interactive geometric correction of feed-forward models using sparse human correspondence clicks. Given an image pair and a small set of cross-view point correspondences provided by a user, Click3R injects geometric constraints into the 3D reconstruction networks through a lightweight point correspondence adapter, enabling the model to resolve ambiguities in challenging scenarios such as repetitive textures, symmetric structures, large viewpoint changes, or textureless regions. To evaluate interactive reconstruction, we curate a challenging benchmark specifically designed to expose failure modes of existing feed-forward 3D reconstruction methods. Experiments show that Click3R reduces reconstruction error with as few as a single correspondence click, improving performance on both standard and our curated benchmarks. Our results highlight that sparse human interaction provides an effective and practical mechanism for correcting geometric reconstruction errors.
作者备注：NeurIPS 2026. Project page: https://uva-computer-vision-lab.github.io/click3r/
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v1 · 2026-11-10 02:58 · 最新观测版本
Click3R: Interactive Stereo 3D Reconstruction with Sparse Correspondence Clicks
Despite impressive progress, existing feed-forward large 3D reconstruction models produce a single, irrevocable prediction. When these models fail, there is no mechanism to correct their mistakes. To address this limitation, we introduce Click3R, a framework for interactive geometric correction of feed-forward models using sparse human correspondence clicks. Given an image pair and a small set of cross-view point correspondences provided by a user, Click3R injects geometric constraints into the 3D reconstruction networks through a lightweight point correspondence adapter, enabling the model to resolve ambiguities in challenging scenarios such as repetitive textures, symmetric structures, large viewpoint changes, or textureless regions. To evaluate interactive reconstruction, we curate a challenging benchmark specifically designed to expose failure modes of existing feed-forward 3D reconstruction methods. Experiments show that Click3R reduces reconstruction error with as few as a single correspondence click, improving performance on both standard and our curated benchmarks. Our results highlight that sparse human interaction provides an effective and practical mechanism for correcting geometric reconstruction errors.
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