U-RED: Unsupervised 3D Shape Retrieval And Deformation For Partial Point Clouds
2023 · Yan di, Chenyangguang Zhang, Ruida Zhang, et al.
Abstract
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target. Considering existing methods typically fail to handle noisy partial observations, U-RED is designed to address this issue from two aspects. First, since one partial shape may correspond to multiple potential full shapes, the retrieval method must allow such an ambiguous one-to-many relationship. Thereby U-RED learns to project all possible full shapes of a partial target onto the surface of a unit sphere. Then during inference, each sampling on the sphere will yield a feasible retrieval. Second, since real-world partial observations usually contain noticeable noise, a reliable learned metric that measures the similarity between shapes is necessary for stable retrieval. In U
Authors
(none)
Tags
Stats
Related papers
- KP-RED: Exploiting Semantic Keypoints For Joint 3D Shape Retrieval And Deformation (2024)8.35
- Joint Learning Of 3D Shape Retrieval And Deformation (2021)11.08
- ROCA: Robust CAD Model Retrieval And Alignment From A Single Image (2021)12.61
- Method For The Generation Of Depth Images For View-based Shape Retrieval Of 3D CAD Model From Partial Point Cloud (2020)9.41
- Extending Deepsdf For Automatic 3D Shape Retrieval And Similarity Transform Estimation (2020)0.00
- Mask2cad: 3D Shape Prediction By Learning To Segment And Retrieve (2020)12.87
- Generalizing Single-view 3D Shape Retrieval To Occlusions And Unseen Objects (2023)5.24
- Coe: Deep Coupled Embedding For Non-rigid Point Cloud Correspondences (2024)0.00