arXiv AI June 2, 2026 By Siyuan Bian, Congrong Xu, Jun Gao

Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation

arXiv:2606. 02552v1 Announce Type: cross Abstract: Despite advances in depth estimation, flying points remain a persistent failure mode: near object boundaries, depth estimators often predict spurious 3D points in the empty space between foreground and background surfaces.

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 AI.

computer-vision

arXiv AI

Jun 30

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

arXiv:2606. 29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid surfaces.

By Xiaohao Xu, Feng Xue, Xiang Li, Haowei Li, Shusheng Yang, Tianyi Zhang, Matthew Johnson-Roberson, Xiaonan Huang

llms computer-vision benchmarks

arXiv AI

Jul 21

Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction

arXiv:2607. 16286v1 Announce Type: cross Abstract: The 3D geometry of real-world scene data is often incomplete.

By Yingzhao Jian, Zihao Lin, Hehe Fan

benchmarks

arXiv Computer Vision

3d ago

Depth Anything in $360^\circ$: Towards Scale Invariance in the Wild

arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360$^\circ$ scene geometry, being essential for robotics and AR/VR applications. While perspective...

By Hualie Jiang, Ziyang Song, Zhiqiang Lou, Rui Xu, Minglang Tan

computer-vision robotics fine-tuning benchmarks

arXiv Machine Learning

Sep 23

CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image

arXiv:2609.25654v1 Announce Type: cross Abstract: Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects...

By Dongwon Son, Junhyek Han, Yoontae Cho, Minseok Lee, Hong-seok Choi, Jiwook Choi, Hyungjin Kim, Beomjoon Kim

robotics

arXiv Computer Vision

Aug 25

SiZeUp: Fast 3D Proxy from Aerial Images via Depth Ordinal Loss

arXiv:2608.22821v1 Announce Type: new Abstract: We present SiZeUp, a fast and scalable approach for constructing large-scale 3D urban proxy models directly from calibrated oblique aerial imagery. Our...

By Wenjun Zhou, Yunshan Li, Qiaoyu Zhu, Weidan Xiong, Hao Zhang, Daniel Cohen-Or, Hui Huang

benchmarks

arXiv Computer Vision

Sep 15

Pixel-wise Planarity for High-Precision Monocular Plane Segmentation

arXiv:2609.13246v1 Announce Type: new Abstract: Plane segmentation from a single RGB image remains challenging due to imprecise region grouping and geometrically inconsistent supervision, often leadi...

By Ahmetcan Yavuz, Alpay Ozkan, R\'emi Pautrat, Shaohui Liu, Marc Pollefeys

computer-vision benchmarks

One email a morning, machine-written

Related stories

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction

Depth Anything in $360^\circ$: Towards Scale Invariance in the Wild

CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image

SiZeUp: Fast 3D Proxy from Aerial Images via Depth Ordinal Loss

Pixel-wise Planarity for High-Precision Monocular Plane Segmentation