Rui Li

Introducing LaRI (ruili3.github.io/lari), a📸single-view,🚀single-feed-forward method to model🙈unseen 3D geometry using layered point maps. It ✅seamlessly extends depth estimation ✅unifies object- & scene-level reasoning ✅builds training & eval datasets Details👇

The current paper submission and review process seems unlikely to survive LLMs. One alternative would be to build a new process around talks: "submission" is making and giving a 30 minute live talk, and "review" is three experts watching, evaluating, and asking questions.

🎺Meet VIST3A — Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator. ➡️ Paper: arxiv.org/abs/2510.13454 ➡️ Website: gohyojun15.github.io/VIST3A/ Collaboration between ETH & Google with Hyojun Go, @DNarnhofer, Goutam Bhat, @fedassa, and Konrad Schindler.

🚀Excited to share our recent work on test-time scaling for feed-forward Gaussian splatting: we learn a recurrent model ReSplat that is able to iteratively improve the reconstruction quality in a feed-forward manner! haofeixu.github.io/resplat/

Interesting ICLR submissions 🤩 Depth Anything 3 - My TLDR: Init multi view transformer of VGGT with later layer DINO weights and use teacher model trained on synthetic data only for pseudo labelling real world datasets openreview.net/forum?id=yirun… Trace Anything - My TLDR: VGGT like model predicting N view geometry and motion as a trajectory field represented using splines and control points openreview.net/forum?id=BqaCh… The field is evolving very fast!

Thanks, AK, for sharing our work!

@MinChonChiSF Made a typo, it’s 2700 accepted papers

🚀 The #ICCV2025 Award Candidate Papers are out! 🚀 From 2,701 submissions, only 13 were selected, spanning 3D vision, generative models, foundation models, and more. Key highlights at a glance 👇

(13/13) Spatially-Varying Autofocus TL; DR: A method for per-pixel autofocus that creates freeform depth-of-field and all-in-focus images. 📃Paper: imaging.cs.cmu.edu/svaf/static/pd… 🏗️Project: imaging.cs.cmu.edu/svaf/

(12/13) Automated Model Evaluation for Object Detection via Prediction Consistency and Reliability TL; DR: A ground-truth-free method (PCR) that evaluates object detectors via prediction consistency and confidence reliability. 📃Paper: arxiv.org/abs/2508.12082