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21 papers in the last four weeks, up 40% on the four weeks before. 0.3% of all new papers.

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MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
Jan 29, 2026Baorui Ma, Jiahui Yang, Donglin Di +53D Perception Camera Intrinsics
Li Auto Inc
Mixtures Closest to a Given Measure: A Semidefinite Programming Approach
Sep 26, 2025Srećko Đurašinović, Jean-Bernard Lasserre, Victor MagronGaussian Mixture Models Convex Relaxation
College of Computing and Data Science, NTU, Singapore · LAAS-CNRS, Toulouse, France · Toulouse School of Economics +1
Constrained PSLQ Search for Machin-like Identities Achieving Record-Low Lehmer Measures
Aug 8, 2025Nick Craig-WoodExact Quadratic Programs
Gibbs randomness-compression proposition
May 29, 2025M. SüzenGibbs Random
Learning from samples: inverse problems over measures
May 11, 2025Francisco Andrade, Gabriel Peyré, Clarice PoonBayesian Inverse Problems Inverse Problem
INRIA (PreMeDICaL & HeKA) · CNRS and ENS, PSL Universit´e · Mathematics Institute, University of Warwick
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
Apr 9, 2024Arkaprabha Basu, Kushal Bose, Sankha Subhra Mullick +2Generative Super-Resolution Generative Adversarial Network
Electronics and Communication Sciences Unit (ECSU), Dolby Laboratories, India · Statistics and Mathematics Unit (SMU), Indian Statistical Institute, Kolkata, India
Structured Approximations of Measures
Oct 13, 2023Keaton Hamm, Varun KhuranaProbability Measures Wasserstein Distance
DEPARTMENT OF MATHEMATICS, THE UNIVERSITY OF TEXAS AT ARLINGTON, ARLINGTON, TX · DIVISION OF DATA SCIENCE, THE UNIVERSITY OF TEXAS AT ARLINGTON, ARLINGTON, TX · DEPARTMENT OF APPLIED MATHEMATICS, BROWN UNIVERSITY, PROVIDENCE, RI 02906.
SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents
Date pendingBingchen Zhao, Dhruv Srikanth, Yuxiang Wu +1Coding Agents Agentic Benchmarks
Weco AI

Jan 29, 2026, cs.CVJ/K move · Enter open · S save

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Baorui Ma, Jiahui Yang, Donglin Di, Xuancheng Zhang+4

Li Auto Inc

3D Perception Camera Intrinsics Scaling Measures Noisy Data

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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. We introduce Metric Anything, a simple and scalable pretraining framework that learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera-specific modeling, or task-specific architectures. Central to our approach is the Sparse Metric Prompt, created by randomly masking depth maps, which serves as a universal interface that decouples spatial reasoning from sensor and camera biases. Using about 20M image-depth pairs spanning reconstructed, captured, and rendered 3D data across 10000 camera models, we demonstrate-for the first time-a clear scaling trend in the metric depth track. The pretrained model excels at prompt-driven tasks such as depth completion, super-resolution and Radar-camera fusion, while its distilled prompt-free student achieves state-of-the-art results on monocular depth estimation, camera intrinsics recovery, single/multi-view metric 3D reconstruction, and VLA planning. We also show that using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence. These results show that metric depth estimation can benefit from the same scaling laws that drive modern foundation models, establishing a new path toward scalable and efficient real-world metric perception. We open-source MetricAnything at http://metric-anything.github.io/metric-anything-io/ to support community research.

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