6D object pose estimation is the problem of identifying the position and orientation of an object relative to a chosen coordinate system, which is a core technology for modern XR applications. State-of-the-art 6D object pose estimators directly predict an object pose given an object observation. Due to the ill-posed nature of the pose estimation problem, where multiple different poses can correspond to a single observation, generating additional plausible estimates per observation can be valuable. To address this, we reformulate the state-of-the-art algorithm GDRNPP and introduce EPRO-GDR (End-to-End Probabilistic Geometry-Guided Regression). Instead of predicting a single pose per detection, we estimate a probability density distribution of the pose. Using the evaluation procedure defined by the BOP (Benchmark for 6D Object Pose Estimation) Challenge, we test our approach on four of its core datasets and demonstrate superior quantitative results for EPRO-GDR on LM-O, YCB-V, and ITODD. Our probabilistic solution shows that predicting a pose distribution instead of a single pose can improve state-of-the-art single-view pose estimation while providing the additional benefit of being able to sample multiple meaningful pose candidates.
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Event cameras are promising devices for lowlatency tracking and high-dynamic range imaging. In this paper,we propose a novel approach for 6 degree-of-freedom (6-DoF)object motion tracking that combines measurements of eventand frame-based cameras. We formulate tracking from highrate events with a probabilistic generative model of the eventmeasurement process of the object. On a second layer, we refinethe object trajectory in slower rate image frames through directimage alignment. We evaluate the accuracy of our approach inseveral object tracking scenarios with synthetic data, and alsoperform e…
Tsinghua University（清华大学）；State Key Lab of Intelligent Technologies and Systems（智能技术与系统国家重点实验室）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Humans are able to perform fast and accurate object pose estimation even under severe occlusion by exploiting learned object model priors from everyday life. However, most recently proposed pose estimation algorithms neglect to utilize the information of object models, often end up with limited accuracy, and tend to fall short in cluttered scenes. In this paper, we present a novel learning-based model, \underline{P}ose-Guided \underline{P}oint Cloud \underline{G}enerating Networks for 6D Object Pose Estimation (P$^2$GNet), designed to effectively exploit object model priors to facilitate 6D ob…
BREATH-VL: Vision-Language-Guided 6-DoF Bronchoscopy Localization via Semantic-Geometric Fusion
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院呼吸与危重症医学科）；Centre of AI and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（香港科学院人工智能与机器人中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
Tsinghua University（清华大学）；Shanghai AI Laboratory（上海人工智能实验室）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）
Dalian University of Technology（大连理工大学）；ByteDance AI Lab（字节跳动人工智能实验室）；Adobe Research（奥多比研究院）；Adobe Systems（奥多比系统公司）
We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module. All modules are differentiable and are learned from data. To train our depth prediction module, we collect a dataset of 2462 RGB-D images captured by mobile phones with a dual-lens camera, and use existing segmentation datasets to improve border prediction. We further leverage a synthetic datas…
Camera pose estimation or camera relocalization is the centerpiece in numerous computer vision tasks such as visual odometry, structure from motion (SfM) and SLAM. In this paper we propose a neural network approach with a graph transformer backbone, namely TransCamP, to address the camera relocalization problem. In contrast with prior work where the pose regression is mainly guided by photometric consistency, TransCamP effectively fuses the image features, camera pose information and inter-frame relative camera motions into encoded graph attributes and is trained towards the graph consistency …
Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Rutgers University（罗格斯大学）
Pancreas segmentation in medical imaging data is of great significance for clinical pancreas diagnostics and treatment. However, the large population variations in the pancreas shape and volume cause enormous segmentation difficulties, even for state-of-the-art algorithms utilizing fully-convolutional neural networks (FCNs). Specifically, pancreas segmentation suffers from the loss of spatial information in 2D methods, and the high computational cost of 3D methods. To alleviate these problems, we propose a probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture, which fu…
EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection
West China Hospital, Sichuan University（四川大学华西医院）；Independent Researcher（独立研究员）；Department of Data Science and Artificial Intelligence, Auckland University of Technology（奥克兰理工大学数据科学与人工智能系）
This paper studies the task of any objects grasping from the known categories by free-form language instructions. This task demands the technique in computer vision, natural language processing, and robotics. We bring these disciplines together on this open challenge, which is essential to human-robot interaction. Critically, the key challenge lies in inferring the category of objects from linguistic instructions and accurately estimating the 6-DoF information of unseen objects from the known classes. In contrast, previous works focus on inferring the pose of object candidates at the instance …
PANS: Probabilistic Airway Navigation System for Real-time Robust Bronchoscope Localization
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, Chinese Academy of Sciences（中国科学院人工智能与机器人中心）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；Dept. of Radiology, The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院放射科）
In this work we present a novel approach to joint semantic localisation and scene understanding. Our work is motivated by the need for localisation algorithms which not only predict 6-DoF camera pose but also simultaneously recognise surrounding objects and estimate 3D geometry. Such capabilities are crucial for computer vision guided systems which interact with the environment: autonomous driving, augmented reality and robotics. In particular, we propose a two step procedure. During the first step we train a convolutional neural network to jointly predict per-pixel globally unique instance la…
University of Technology Sydney（悉尼科技大学）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；University of Trento（特伦托大学）
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs in the case of partial overlap is still a challenge. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters. We reformulate the registration problem as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We intro…
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, THBI Lab（计算机科学与技术系，人工智能研究院，BNRist中心，THBI实验室）；Tsinghua-Bosch Joint ML Center, Tsinghua University, Beijing, 100084 China（清华大学-博世联合机器学习中心，清华大学，北京，100084中国）；Gaoling School of Artificial Intelligence, Renmin University of China（北京人工智能学院，中国人民大学）；Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China（北京大数据管理与分析方法重点实验室，北京，中国）
Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model
Washington University in St. Louis（圣路易斯华盛顿大学）；Yale University（耶鲁大学）；Western University（西安大略大学）；Lawson Health Research Institute（劳森健康研究所）；McGill University（麦吉尔大学）；Siemens Healthineers AG（西门子医疗保健公司）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）
LLM-Guided Probabilistic Fusion for Label-Efficient Document Layout Analysis
Department of Computer Science, Iowa State University（计算机科学系，爱荷华州立大学）；Department of Civil, Construction & Environmental Engineering, Iowa State University（土木、建设与环境工程系，爱荷华州立大学）
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）
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data. Recently, probabilistic localization systems utilizing appearance-invariant visual place recognition (VPR) methods as the primary exteroceptive sensor have demonstrated state-of-the-art performance in the presence of substantial appearance change. However, existing systems 1) do not fully utilize odometry data within the motion models, and 2) are unable to handle route deviations, due to the ass…
Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration
National Engineering Research Center for Robot Visual Perception and Control Technology, School of Artificial Intelligence and Robotics, Hunan University（机器人视觉感知与控制技术国家工程研究中心，人工智能与机器人学院，湖南大学）；School of Architecture and Art, Central South University（建筑与艺术学院，中南大学）；School of Computing and Communications, Lancaster University（计算与通讯学院，兰卡斯特大学）；Department of Computer Science and Software Engineering, The University of Western Australia（计算机科学与软件工程系，西澳大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）
NVIDIA（英伟达）；Georgia Institute of Technology（佐治亚理工学院）
We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as input the previous and current frame from a monocular RGB video, as well as predictions from the previous frame, to predict the bounding cuboid and 6-DoF pose (up to scale). Internally, a deep network predicts distributions over object keypoints (vertices of the bounding cuboid) in image coordinates, after which a novel probabilistic filtering process integrates across estimates before computing the final pose using P…
Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction
Department of Electrical \& Computer Engineering, Rice University, Houston TX, USA Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston TX, USA
Zhejiang University（浙江大学）；Hikvision Research Institute（海康威视研究院）；National University of Singapore（新加坡国立大学）；Chongqing University（重庆大学）；Hainan Institute of Zhejiang University（浙江大学海南研究院）
Self-training for unsupervised domain adaptive object detection is a challenging task, of which the performance depends heavily on the quality of pseudo boxes. Despite the promising results, prior works have largely overlooked the uncertainty of pseudo boxes during self-training. In this paper, we present a simple yet effective framework, termed as Probabilistic Teacher (PT), which aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner. Specifically, we propose to leverage the uncertainty-g…
MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；MIT（麻省理工学院）；Stanford University（斯坦福大学）
We introduce a visually-guided and physics-driven task-and-motion planning benchmark, which we call the ThreeDWorld Transport Challenge. In this challenge, an embodied agent equipped with two 9-DOF articulated arms is spawned randomly in a simulated physical home environment. The agent is required to find a small set of objects scattered around the house, pick them up, and transport them to a desired final location. We also position containers around the house that can be used as tools to assist with transporting objects efficiently. To complete the task, an embodied agent must plan a sequence…
