9 papers
OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World
Katherine Liu, Sergey Zakharov, Dian Chen +4
We would like to estimate the pose and full shape of an object from a single observation, without assuming known 3D model or category. In this work, we propose OmniShape, the first…
GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity
Takuya Ikeda, Sergey Zakharov, Muhammad Zubair Irshad +8
We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video. Existing methods, while achieving impressive results, often strugg…
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping
Shun Iwase, Zubair Irshad, Katherine Liu +8
Robotic grasping is a cornerstone capability of embodied systems. Many methods directly output grasps from partial information without modeling the geometry of the scene, leading t…
Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematics
Tianyi Ko, Takuya Ikeda, Balazs Opra +1
Grasp detection methods typically target the detection of a set of free-floating hand poses that can grasp the object. However, not all of the detected grasp poses are executable d…
A Planar-Symmetric SO(3) Representation for Learning Grasp Detection
Tianyi Ko, Takuya Ikeda, Hiroya Sato +1
Planar-symmetric hands, such as parallel grippers, are widely adopted in both research and industrial fields. Their symmetry, however, introduces ambiguity and discontinuity in the…
ViFu: Multiple 360 Objects Reconstruction with Clean Background via Visible Part Fusion
Tianhan Xu, Takuya Ikeda, Koichi Nishiwaki
In this paper, we propose a method to segment and recover a static, clean background and multiple 360 objects from observations of scenes at different timestamps. Recent wo…
DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation
Takuya Ikeda, Sergey Zakharov, Tianyi Ko +5
This paper addresses the challenging problem of category-level pose estimation. Current state-of-the-art methods for this task face challenges when dealing with symmetric objects a…
Gravity-aware Grasp Generation with Implicit Grasp Mode Selection for Underactuated Hands
Tianyi Ko, Takuya Ikeda, Thomas Stewart +2
Learning-based grasp detectors typically assume a precision grasp, where each finger only has one contact point, and estimate the grasp probability. In this work, we propose a data…
GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence
Pengyuan Wang, Takuya Ikeda, Robert Lee +1
Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. Recently, deep-learning-based approaches have made great prog…