← Back to all news
arXiv Computer Vision By Jingnan Gao, Jiahui Huang, Sven Elflein, Jianhao Zheng, Bingxin Ke, Xiaokang Yang, Yichao Yan, Laura Leal-Taix\'e, Qunjie Zhou, Jiawei Ren, Shengyu Huang

MetricTrail: Revisiting the Power of Metric Depth and Local Optimization for Long-Sequence Streaming 3D Reconstruction

Read the original on arXiv Computer Vision →

MetricTrail is a streaming system that accurately estimates camera motion and performs dense 3D reconstruction from long monocular videos. It leverages single‑image metric depth to improve trajectory accuracy without retraining the correspondence network or enlarging the optimization window, and uses learned adapters to enhance feature matching with frozen DINOv3 representations. The modular design allows independent upgrades of the depth provider, supports supplied camera intrinsics and calibrated stereo data, and achieves the lowest average trajectory error on KITTI, Oxford Spires, and VBR under RGB‑only protocols while delivering strong dense reconstructions on indoor, outdoor, and in‑the‑wild benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
21h ago

Kineo: Calibration-Free Metric Motion Capture From Sparse RGB Cameras

Kineo is a fully automatic, calibration‑free pipeline that performs markerless motion capture using unsynchronized, uncalibrated consumer‑grade RGB cameras. It uses 2D keypoints from existing detectors to jointly calibrate cameras (including distortion), reconstruct 3D keypoints, and generate dense scene pointmaps at metric scale. The method introduces confidence‑aware subsampling, graph‑based optimization, and a pairwise reprojection consistency score to achieve robust calibration and accurate metric scale recovery, achieving significant error reductions on EgoHumans and Human3.6M datasets while remaining computationally efficient.

By Charles Javerliat, Pierre Raimbaud, Guillaume Lavou\'e
benchmarks
More like this →
arXiv AI
Jul 7

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

arXiv:2601. 22054v2 Announce Type: replace-cross Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data.

By Baorui Ma, Jiahui Yang, Donglin Di, Xuancheng Zhang, Jianxun Cui, Hao Li, Yan Xie, Wei Chen
llmscomputer-visionmultimodalbenchmarks
More like this →