Object Geometry

Object geometry research focuses on accurately representing and reconstructing the 3D shapes of objects from various input modalities, such as RGB images, depth maps, and multi-view data. Current efforts concentrate on improving the robustness and efficiency of 3D reconstruction algorithms, particularly for challenging scenarios like handling object occlusion, intra-class variations, and scale ambiguity, often employing neural networks, graph-based methods, and novel geometric representations like Laplacian mixtures or sweep surfaces. These advancements have significant implications for robotics, augmented reality, and computer vision applications requiring precise 3D scene understanding, enabling tasks such as object manipulation, pose estimation, and realistic scene synthesis.