The framework processes multiple geometric tasks from video inputs.
A year ago I argued ML saved CV. CVPR’26 best paper D4RT shows how vision isn’t being blindly assimilated by AI - it’s undergoing architectural consolidation. Unifying tracking, depth&pose into a 4D model speeds up pose estimation 100x. Geometric vision remains alive & relevant
Users are excited about the CVPR Best Paper unifying tracking, depth, and pose in 4D models because it highlights expanding scientific progress into higher dimensions.
A delightful surprise! Great work by the team pushing the frontier of unified 4D perception: estimating depth, camera pose, 3D point tracks, and point clouds from videos.
Announcing the official #CVPR2026 Best Paper Award Winner! 🏆 Congratulations to the authors for their landmark contributions to the field!👏👏
A year ago I argued ML saved CV. CVPR’26 best paper D4RT shows how vision isn’t being blindly assimilated by AI - it’s undergoing architectural consolidation. Unifying tracking, depth&pose into a 4D model speeds up pose estimation 100x. Geometric vision remains alive & relevant
@docmilanfar Hehe, I agree, there is so much more space in 4 dimensions. Not mentioning 5 and more :) I start to see that science is noticing more and more and how deep it can be :)
The framework processes multiple geometric tasks from video inputs.
A year ago I argued ML saved CV. CVPR’26 best paper D4RT shows how vision isn’t being blindly assimilated by AI - it’s undergoing architectural consolidation. Unifying tracking, depth&pose into a 4D model speeds up pose estimation 100x. Geometric vision remains alive & relevant