This paper presents a novel camera relocalization method, STDLoc, which leverages Feature Gaussian as scene representation. STDLoc is a full relocalization pipeline that can achieve accurate relocalization without relying on any pose prior. Unlike previous coarse-to-fine localization methods that require image retrieval first and then feature matching, we propose a novel sparse-to-dense localization paradigm. Based on this scene representation, we introduce a novel matching-oriented Gaussian sampling strategy and a scene-specific detector to achieve efficient and robust initial pose estimation. Furthermore, based on the initial localization results, we align the query feature map to the Gaussian feature field by dense feature matching to enable accurate localization. The experiments on indoor and outdoor datasets show that STDLoc outperforms current state-of-the-art localization methods in terms of localization accuracy and recall.
University of Quebec at Montreal（魁北克大学蒙特利尔分校）；University of Bahrain（巴林大学）
We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previous approaches built a scene-dependent model that explicitly or implicitly embeds the structure of the scene. They use convolution neural networks or decision trees to establish 2D/3D-3D correspondences. Such a mapping overfits the target scene and does not generalize well to dynamic changes in the environment. Our work introduces a novel approach to solve the camera relocalization problem by using the available triang…
Camera relocalization involving a prior 3D reconstruction plays a crucial role in many mixed reality and robotics applications. Estimating the camera pose directly with respect to pre-built 3D models can be prohibitively expensive for several applications with limited storage and/or communication bandwidth. Although recent scene and absolute pose regression methods have become popular for efficient camera localization, most of them are computation-resource intensive and difficult to obtain a real-time inference with high accuracy constraints. This study proposes a simple scene regression metho…
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 …
University of British Columbia（不列颠哥伦比亚大学）；Google Research（谷歌研究院）；Google DeepMind（谷歌DeepMind）；Simon Fraser University（西蒙菲莎大学）；University of Toronto（多伦多大学）
EOGS++: Earth Observation Gaussian Splatting with Internal Camera Refinement and Direct Panchromatic Rendering
Universite Paris-Saclay, CNRS, ENS Paris-Saclay, Centre Borelli（巴黎萨克雷大学，法国国家科学研究中心，巴黎萨克雷高等师范学校，博雷利中心）；Politecnico di Torino（都灵理工大学）；AMIAD, Pôle Recherche（AMIAD，研究部）；Institut Universitaire de France（法兰西大学研究院）
D2S: Representing sparse descriptors and 3D coordinates for camera relocalization
Graduate School of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程研究生院）；School of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）
GSPotential: Camera Potential Field for Sparse-View 3D Gaussian Splatting
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Zhongguancun Laboratory（中关村实验室）
Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting
Department of Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能交叉学科项目系，首尔国立大学）；Department of Artificial Intelligence, Yonsei University（人工智能系，延世大学）
EndoMD-SLAM: Endoscopic Gaussian Splatting SLAM under Optical Degradation with Memory and Static-Transient Decomposition
Texas A&M University（德克萨斯农工大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Minnesota（明尼苏达大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
