REVIEW 2 cited by
Score-PA: Score-based 3D Part Assembly
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Autonomous 3D part assembly is a challenging task in the areas of robotics and 3D computer vision. This task aims to assemble individual components into a complete shape without relying on predefined instructions. In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly. Knowing that score-based methods are typically time-consuming during the inference stage. To address this issue, we introduce a novel algorithm called the Fast Predictor-Corrector Sampler (FPC) that accelerates the sampling process within the framework. We employ various metrics to assess assembly quality and diversity, and our evaluation results demonstrate that our algorithm outperforms existing state-of-the-art approaches. We release our code at https://github.com/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly.
Forward citations
Cited by 2 Pith papers
-
Geometric Point Attention Transformer for 3D Shape Reassembly
GPAT is a transformer with geometric point attention and an iterative recycling scheme that improves 6-DoF part pose prediction on both semantic and geometric 3D shape assembly benchmarks.
-
Leveraging Pretrained Diffusion Models for Zero-Shot Part Assembly
The authors propose a zero-shot part assembler that repeatedly denoises a perturbed assembly with a pretrained diffusion model and aligns each part to the denoised cloud using ICP.
Discussion (0). Continue with ORCID to comment.