The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；LIGHTSPEED；The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）
Promptable instance segmentation is widely adopted in embodied and AR systems, yet the performance of foundation models trained on perspective imagery often degrades on 360° panoramas. In this paper, we introduce Segment Any 4K Panorama (SAP), a foundation model for 4K high-resolution panoramic instance-level segmentation. We reformulate panoramic segmentation as fixed-trajectory perspective video segmentation, decomposing a panorama into overlapping perspective patches sampled along a continuous spherical traversal. This memory-aligned reformulation preserves native 4K resolution while restoring the smooth viewpoint transitions required for stable cross-view propagation. To enable large-scale supervision, we synthesize 183,440 4K-resolution panoramic images with instance segmentation labels using the InfiniGen engine. Trained under this trajectory-aligned paradigm, SAP generalizes effectively to real-world 360° images, achieving +17.2 zero-shot mIoU gain over vanilla SAM2 of different sizes on real-world 4K panorama benchmark.
With the recent proliferation of consumer-grade 360° cameras, it is worth revisiting visual perception challenges with spherical cameras given the potential benefit of their global field of view. To this end we introduce a spherical convolutional hourglass network (SCHN) for the dense labeling on the sphere. The SCHN is invariant to camera orientation (lifting the usual requirement for `upright' panoramic images), and its design is scalable for larger practical datasets. Initial experiments show promising results on a spherical semantic segmentation task.
Deep learning algorithms have recently achieved promising deraining performances on both the natural and synthetic rainy datasets. As an essential low-level pre-processing stage, a deraining network should clear the rain streaks and preserve the fine semantic details. However, most existing methods only consider low-level image restoration. That limits their performances at high-level tasks requiring precise semantic information. To address this issue, in this paper, we present a segmentation-aware progressive network (SAPNet) based upon contrastive learning for single image deraining. We star…
National Tsing Hua University（国立清华大学）；ASUS AICS（华硕AI研发中心）；Joint Research Center for AI Technology and All Vista Healthcare（AI技术与全视野医疗联合研究中心）；Aeolus Robotics
We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact LHFeat flattens the features along the vertical direction and has shown success in modeling per-column modality for room layout reconstruction. HoHoNet advances in two important aspects. First, the deep architecture is redesigned to run faster with improved accuracy. Second, we propose a novel horizon-to-dense module, which relaxes the per-column output shape constraint, allowing per-pixel dense prediction from LHFeat. HoHoN…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；ByteDance Inc.（字节跳动公司）；Carl Zeiss AG（卡尔·蔡司股份公司）
Panoramic images with their 360-degree directional view encompass exhaustive information about the surrounding space, providing a rich foundation for scene understanding. To unfold this potential in the form of robust panoramic segmentation models, large quantities of expensive, pixel-wise annotations are crucial for success. Such annotations are available, but predominantly for narrow-angle, pinhole-camera images which, off the shelf, serve as sub-optimal resources for training panoramic models. Distortions and the distinct image-feature distribution in 360-degree panoramas impede the transfe…
Parallax-tolerant Image Stitching via Segmentation-guided Multi-homography Warping
College of Information Science and Engineering, Henan University of Technology（河南工业大学信息科学与工程学院）；Hong Kong University of Science and Technology（香港科技大学）；School of Mathematical Sciences, Shenzhen University（深圳大学数学科学学院）
Beijing Jiaotong University（北京交通大学）；Institute of Information Science（信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（北京先进信息科学与网络技术重点实验室）
Recently, horizontal representation-based panoramic semantic segmentation approaches outperform projection-based solutions, because the distortions can be effectively removed by compressing the spherical data in the vertical direction. However, these methods ignore the distortion distribution prior and are limited to unbalanced receptive fields, e.g., the receptive fields are sufficient in the vertical direction and insufficient in the horizontal direction. Differently, a vertical representation compressed in another direction can offer implicit distortion prior and enlarge horizontal receptiv…
LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark
organization= College of Information Science；Technology \& Artificial Intelligence, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= Department of Geography, College of Natural Resources；organization= Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= School of Architecture \& Urban Planning, Shenzhen University , city= Shenzhen , postcode= 518060 , state= Guangdong , country= China；organization= School of Software, Hefei University of Technology , city= Hefei , postcode= 230601 , state= Anhui , country= China
Beijing Jiaotong University（北京交通大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Existing panoramic depth estimation methods based on convolutional neural networks (CNNs) focus on removing panoramic distortions, failing to perceive panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the panorama transformer (named PanoFormer) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and panorama specific metrics. In particular, we divide patches on the spherical tangent domain into tokens to reduce the negative effect of panoramic distortions. Since the geometric structures are es…
TU Delft（代尔夫特理工大学）；TU Eindhoven（埃因霍温理工大学）；Cyclomedia B.V（Cyclomedia公司）
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pedestrians, vehicles) in street-view imagery. We propose a novel moving object segmentation algorithm exploiting consistencies in depth across multiple street-view images that are later combined with the results of a segmentation network. The detected moving objects are removed and inpainted with information from other views, to obtain a realistic out…
First Place Solution to the MLCAS 2025 GWFSS Challenge: The Devil is in the Detail and Minority
National Key Laboratory of Multispectral Information Intelligent Processing Technology（multispectral information intelligent processing technology national key laboratory）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Huazhong University of Science and Technology（华中科技大学）
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Hunan University（湖南大学）；University of Oxford（牛津大学）；Zhejiang University（浙江大学）；ByteDance Inc.（字节跳动公司）；Beihang University（北京航空航天大学）
SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ARC Lab, Tencent PCG（腾讯平台与内容事业群ARC实验室）；Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校研究院）；Zhejiang – Singapore Innovation and AI Joint Research Lab（浙江-新加坡创新与人工智能联合实验室）
UniFS: Universal Few-shot Instance Perception with Point Representations
The University of Hong Kong（香港大学）；SenseTime Research and Tetras.AI（商汤科技研究院与Tetras.AI）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China（合成自动化过程工业国家重点实验室，东北大学，沈阳，中国）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Surrey Institute for People-Centred Artificial Intelligence, and Centre for Vision, Speech and Signal Processing, University of Surrey（以人为中心的人工智能 Surrey 院，以及视觉、语音和信号处理中心， Surrey 大学）；College of Computing & Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
