arXiv Computer Vision
6d ago

LLPR: Location-aware learning and physics-based reconstruction for raindrop removal from a single image

The paper introduces LLPR, a framework that integrates location-aware learning and physics-based reconstruction to remove raindrops from single images. It replaces costly preprocessing masks with a learnable branch that can be discarded during inference, and reconstructs the background by first estimating a transparency matrix and raindrop layer using a physical model. The authors also present a new real-world dataset and show that LLPR outperforms existing state‑of‑the‑art methods.

By Zewei He, Xingyu Liu, Xing Luo, Guizhong Fu, Zixuan Chen, Yu Chen, Jinlei Li, Zhe-Ming Lu
arXiv Computer Vision
Aug 25

LoViF 2026 The First Challenge on Unified Removal of Raindrops and Reflections: Methods and Results

arXiv:2608.22723v1 Announce Type: new Abstract: This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a freque...

By Zewei He, Xi Tong, Yu Chen, Xingyu Liu, Xin Li, Zepeng Wang, Jiagao Hu, Fuhao Li, Yuxuan Chen, Fei Wang, Daiguo Zhou, Minmin Yi, Chuanrui Zhang, Liwen Zhang, Yeongjin Jeong, Hyunjin Cho, Jiwon Lee, Minsang Kim, Jae Woong Soh, Jin-Hui Jiang, Rong-Lin Jian, Chih-Chung Hsu, Youngjin Oh, Junhyeong Kwon, Junyoung Park, Jae Hyun Park, Sung Ju Lee, Nam Ik Cho, Vishwajeet Shukla, Himanshu Baurai, Zhiqi Zhang, Kui Jiang, Zhaocheng Yu, Runzhe Li, Dawei Fan, Hao Li, Zhanshuo Zhang, Fan Ji, Jiangmeng Li, Xiongxin Tang, Fanjiang Xu, Shangquan Sun, Anh-Kiet Duong, Petra Gomez-Kr\"amer, Jean-Michel Carozza, Ruibo Zhang, Dexiang Hong, Xinyan Liu, Shengeng Tang, Weidong Chen, Tzu-Hsuan Weng, Min-Te Sun
arXiv Computer Vision
Sep 7

Weather-Conditioned Depth Anything

Weather-Conditioned Depth Anything (DA‑W) is a new framework that enhances monocular depth estimation models, like the Depth Anything series, to perform robustly under adverse weather conditions such as fog, rain, snow, and low‑light. It achieves this by disentangling style from content: a Style Filter extracts weather‑specific embeddings from a curated mix of real and synthetic degradation data, which are then injected into the backbone via a lightweight, zero‑initialized adapter. The adapter is trained with pseudo‑label distillation and alignment, enabling a single unified model to adapt to diverse weather scenarios while preserving its generalization on clean data, and it achieves state‑of‑the‑art performance with an average 3.7% improvement in AbsRel on weather benchmarks.

By Zhaoming Xu, Chan-Wei Hu, Kuan-Ru Huang, Zihao Zhu, Renjie Li, Yang Zhou, Zhengzhong Tu