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: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
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
arXiv:2504.10201v3 Announce Type: replace
Abstract: In this paper, we introduce a synthetic image generator relying on a few simple principles, specifically focusing on geometric modeling, textures,...
By Raphael Achddou, Yann Gousseau, Sa\"id Ladjal, Sabine S\"usstrunk
arXiv:2606. 02310v1 Announce Type: cross Abstract: Flooding is the most pervasive natural disaster worldwide.
By Yogesh Bhattarai, Vijay Chaudhary, Wai Lim Kim, Sanjib Sharma
arXiv:2610.02000v1 Announce Type: new
Abstract: Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather...
By Hossein Maghsoumi, George Atia, Yaser P. Fallah
Reliable perception under diverse weather conditions remains a major challenge for autonomous driving systems. A common strategy to improve robustness is either to synthesize adverse weather conditions for training perception models or to apply weather-removal techniques to recover clean inputs.
Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the literature, including the use of median, Gaussian, and bilateral filters.
arXiv:2609.36810v1 Announce Type: new
Abstract: Video world models aim to predict future content from an observed scene while following prescribed camera motion. Real-world scene evolution is determi...
By Renlong Wu, Guanqiao Wang, Xuan Shang, Yin Hanming, Xiaoxiao Sheng, Tianyu Huang, Hui Li, Wangmeng Zuo
arXiv:2403. 06025v4 Announce Type: replace-cross Abstract: We introduce a new approach using computer vision to predict the land surface displacement from subsurface geometry images for Carbon Capture and Sequestration (CCS).
By Wei Chen, Yunan Li, Yuan Tian
arXiv:2608. 03822v1 Announce Type: cross Abstract: Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation.
By Zhang Weihui, Wang Ruizhi, Xu Hongye, Wang Huiqiong, Sun Li, Song Mingli
The paper introduces RaiNet, a data‑driven model that jointly learns multiscale water‑quality dynamics and station‑specific rainfall effects. It uses LocTrend to capture irregular water‑quality patterns, constructs station‑oriented rainfall events from gridded precipitation, and applies XGateFusion for lag‑aware fusion across scales. Experiments on three new multimodal datasets show RaiNet surpasses existing time‑series, water‑quality, diffusion‑based, and spatiotemporal models by over 20%, with each module contributing uniquely to performance.
By Ziqi Wang, Hailiang Zhao, Cheng Bao, Daojiang Hu, Wenzhuo Qian, Shuiguang Deng