14 citations · 36 across the 6 of their papers we have counts for
13 papers · 1 filter
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks
Yijia Weng, Kaichun Mo, Ruoxi Shi +2
Some extremely low-dimensional yet crucial geometric eigen-lengths often determine the success of some geometric tasks. For example, the height of an object is important to measure…
Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing
Jeonghyun Kim, Kaichun Mo, Minhyuk Sung +1
We propose Seg&Struct, a supervised learning framework leveraging the interplay between part segmentation and structure inference and demonstrating their synergy in an integrated f…
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
Yining Hong, Kaichun Mo, Li Yi +4
This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D object…
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning
Kaichun Mo, Yuzhe Qin, Fanbo Xiang +2
Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics,…
Where2Act: From Pixels to Actions for Articulated 3D Objects
Kaichun Mo, Leonidas Guibas, Mustafa Mukadam +2
One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment. In this paper, we take a step towards that long-term goal -- w…
Compositionally Generalizable 3D Structure Prediction
Songfang Han, Jiayuan Gu, Kaichun Mo +4
Single-image 3D shape reconstruction is an important and long-standing problem in computer vision. A plethora of existing works is constantly pushing the state-of-the-art performan…