iCaps: Iterative Category-level Object Pose and Shape Estimation
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Washington（华盛顿大学）；NVIDIA（英伟达）
Xinke Deng, Junyi Geng, Timothy Bretl, Yu Xiang, Dieter Fox
This paper proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shapes. We develop a category-level auto-encoder network using depth images as input, where feature embeddings from the auto-encoder encode poses of objects in a category. The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category. By exploiting an implicit shape representation based on signed distance functions, we build a LatentNet to estimate a latent representation of the 3D shape given the estimated pose of an object. Then the estimated pose and shape can be used to update each other in an iterative way. Our category-level 6D object pose and shape estimation pipeline only requires 2D detection and segmentation for initialization. We evaluate our approach on a publicly available dataset and demonstrate its effectiveness. In particular, our method achieves comparably high accuracy on shape estimation.
Universal Features Guided Zero-Shot Category-Level Object Pose Estimation
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Towards Adaptive Open-Set Object Detection via Category-Level Collaboration Knowledge Mining
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Interdisciplinary Institute of Artificial Intelligence, Faculty of Infor-X, Xidian University（西安电子科技大学信息交叉研究院人工智能跨学科研究所）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
ETH Zürich（苏黎世联邦理工学院）；Max Planck ETH Center for Learning Systems（马克斯·普朗克学习系统中心）；University of Tübingen（蒂宾根大学）；Max Planck Institute for Intelligent Systems, Tübingen（马克斯·普朗克智能系统研究所（蒂宾根））
LVLMs as inspectors: an agentic framework for category-level structural defect annotation
National Key Laboratory of Water Disaster Prevention, Hohai University（河海大学水灾害防御全国重点实验室）；College of Water Conservancy and Hydropower Engineering, Hohai University（河海大学水利水电学院）；College of Artificial Intelligence and Automation, Hohai University（河海大学人工智能与自动化学院）；School of Civil Engineering, The University of Sydney（悉尼大学土木工程学院）
IterInv: Iterative Inversion for Pixel-Level T2I Models
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Optics and Electronics（光电技术研究所）；Computer Vision Center（计算机视觉中心）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）
Stanford University（斯坦福大学）；Megvii (Face++) Research（旷视（Face++）研究院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
