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SpaCeFormer: Fast Proposal-Free Open-Vocabulary 3D Instance Segmentation
Chris Choy, Junha Lee, Chunghyun Park +2
Open-vocabulary 3D instance segmentation is a core capability for robotics and AR/VR, but prior methods trade one bottleneck for another: multi-stage 2D+3D pipelines aggregate foun…
Affostruction: 3D Affordance Grounding with Generative Reconstruction
Chunghyun Park, Seunghyeon Lee, Minsu Cho
This paper addresses the problem of affordance grounding from RGBD images of an object, which aims to localize surface regions corresponding to a text query that describes an actio…
Combinative Matching for Geometric Shape Assembly
Nahyuk Lee, Juhong Min, Junhong Lee +2
This paper introduces a new shape-matching methodology, combinative matching, to combine interlocking parts for geometric shape assembly. Previous methods for geometric assembly ty…
Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Junha Lee, Eunha Park, Chunghyun Park +2
Affordance grounding aims to localize where to interact with an object, a fundamental capability for embodied agents. Yet progress is bottlenecked by data: manual annotation is pro…
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation
Junha Lee, Chunghyun Park, Jaesung Choe +4
We tackle open-vocabulary 3D scene understanding by introducing a novel data generation pipeline and training framework. Our method addresses three critical requirements for effect…
Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform
Chunghyun Park, Seungwook Kim, Jaesik Park +1
Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics. However, existing self-supervised…
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
Seungwook Kim, Chunghyun Park, Yoonwoo Jeong +2
Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely va…
Fast Point Transformer
Chunghyun Park, Yoonwoo Jeong, Minsu Cho +1
The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approach…
PointMixer: MLP-Mixer for Point Cloud Understanding
Jaesung Choe, Chunghyun Park, Francois Rameau +2
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer. Despite its simplicity compared to transformer, the concept of channel-mixing MLPs and t…