CVPR

ExMesh: Explicit Mesh Reconstruction with Topology Adaptation

ExMesh: 具有拓扑自适应的显式网格重建

Chuanjin Fan, Lifan Wu, Wenjie Chang, Hanzhi Chang, Wenfei Yang, Tianzhu Zhang

机构 * University of Science and Technology of China（中国科学技术大学） ； National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory（国家空间科学探测重点实验室，深空探测实验室） 纠错

AI总结 提出ExMesh框架，通过可微优化与离散拓扑更新直接优化显式网格，引入自适应顶点分裂合并和实时UV维护，实现从粗到细的优化，兼顾精度、效率和网格简洁性。

Comments Accepted at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026); Project page: https://fan-treasure.github.io/ExMesh_page.github.io/

Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light

使离散的成为连续的：合成RAW增强用于细粒度评估人检测性能在低光环境

Valeria Pais, Malena Mendilaharzu, Daniele Faccio, Luis Oala, Christoph Clausen, Bruno Sanguinetti

机构 * University of Glasgow（格拉斯哥大学） ； Dotphoton 纠错

AI总结 本文提出了一种合成RAW增强方法，用于在低光条件下更准确地评估人检测模型的性能，通过生成与相机传感器噪声模型匹配的低光样本，以改善基准测试的数据覆盖。

Comments Accepted non-archival paper at the CVPR 2026 AUTOPILOT Workshop (Autonomous Understanding Through Open-world Perception and Integrated Language Models for On-road Tasks)

EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection

EReCu: 多线索学习下的伪标签演化融合与精炼用于无监督伪装检测

Shuo Jiang, Gaojia Zhang, Min Tan, Yufei Yin, Gang Pan

AI总结 EReCu提出了一种统一的无监督伪装检测框架，通过多线索学习提升伪标签可靠性与特征保真度，结合原生感知模块、伪标签演化融合和局部精炼，实现高精度的伪装检测。

Comments Accepted by CVPR 2026

Classification Drives Geographic Bias in Street Scene Segmentation

分类导致街景分割中的地理偏差

Rahul Nair, Gabriel Tseng, Esther Rolf, Bhanu Tokas, Hannah Kerner

机构 * Arizona State University（亚利桑那州立大学） ； Mila Quebec AI Institute（米拉魁北克人工智能研究所） ； University of Colorado Boulder（科罗拉多大学博尔德分校） 纠错

AI总结 该研究针对实例分割任务，发现以欧洲驾驶场景训练的模型存在地理偏差，且偏差源于分类错误，通过粗粒度类别分组可缓解此偏差。

Comments Accepted at the CVPR 2025 Workshop on Fair, Data-Efficient, and Trusted Computer Vision

Journal ref R. Nair, B. Tokas, G. Tseng, E. Rolf and H. Kerner, "Classification Drives Geographic Bias in Street Scene Segmentation," 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)