OPDMulti: Openable Part Detection for Multiple Objects
Reviewed by Pithpith:N2J3LSQRopen to challenge →
read the original abstract
Openable part detection is the task of detecting the openable parts of an object in a single-view image, and predicting corresponding motion parameters. Prior work investigated the unrealistic setting where all input images only contain a single openable object. We generalize this task to scenes with multiple objects each potentially possessing openable parts, and create a corresponding dataset based on real-world scenes. We then address this more challenging scenario with OPDFormer: a part-aware transformer architecture. Our experiments show that the OPDFormer architecture significantly outperforms prior work. The more realistic multiple-object scenarios we investigated remain challenging for all methods, indicating opportunities for future work.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
ArtMesh: Part-Aware Articulated Mesh Fields with Motion-Consistent Dynamics
ArtMesh presents a mesh-native pipeline for articulated reconstruction that uses restricted Delaunay remeshing and bidirectional motion consistency to outperform 3D Gaussian Splatting methods on joint estimation and p...
-
PAOLI: Pose-free Articulated Object Learning from Sparse-view Images
A pipeline that reconstructs articulated objects from sparse unposed images by aligning independent per-pose reconstructions via learned deformation fields and progressive static/moving part disentanglement.
-
GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation
GSAM integrates vision perceiver, VLM refiner with CoT, LLM constraint generator, and kinematic planner to raise success rate by 36% and cut variance by 3.1% on 50 hinge tasks across 5 categories.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.