PReP: Efficient context-based shape retrieval for missing parts
Not on the map. Why?
Abstract (arXiv)
In this paper we study the problem of shape part retrieval in the point cloud domain. Shape retrieval methods in the literature rely on the presence of an existing query object, but what if the part we are looking for is not available? We present Part Retrieval Pipeline (PReP), a pipeline that creatively utilizes metric learning techniques along with a trained classification model to measure the suitability of potential replacement parts from a database, as part of an application scenario targeting circular economy. Through an innovative training procedure with increasing difficulty, it is able to learn to recognize suitable parts relying only on shape context. Thanks to its low parameter size and computational requirements, it can be used to sort through a warehouse of potentially tens of thousand of spare parts in just a few seconds. We also establish an alternative baseline approach to compare against, and extensively document the unique challenges associated with this task, as well as identify the design choices to solve them.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 59 references that are in the map. First posted on arXiv 18 Oct 2024.
Builds on
33 of 59 references.
References · 33
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 971
- 2020PCT: Point cloud transformercited by 732
- 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 526
- 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
- 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 403
- 2018PCN: Point Completion Networkcited by 384
- 2019Multi-Similarity Loss with General Pair Weighting for Deep Metric Learningcited by 368
- 2017A Neural Representation of Sketch Drawingscited by 313
- 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 286
21 more
- 2017Sampling Matters in Deep Embedding Learningcited by 285
- 2023Zero-Shot Composed Image Retrieval with Textual Inversioncited by 176
- 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
- 2019SoftTriple Loss: Deep Metric Learning Without Triplet Samplingcited by 163
- 2019Cross-Batch Memory for Embedding Learningcited by 132
- 2017Deep Metric Learning with Angular Losscited by 107
- 2019Improved Embeddings with Easy Positive Triplet Miningcited by 67
- 2021Joint Learning of 3D Shape Retrieval and Deformationcited by 42
- 20223D Part Assembly Generation with Instance Encoded Transformercited by 19
- 2022Boosting vision transformers for image retrievalcited by 19
- 2019View N-gram Network for 3D Object Retrievalcited by 18
- 2022SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wildcited by 9
- 2020Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learningcited by 8
- 2023Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objectscited by 8
- 2023Unsupervised 3D Shape Reconstruction by Part Retrieval and Assemblycited by 7
- 2024Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Modelscited by 7
- 20223D Shape Knowledge Graph for Cross-domain 3D Shape Retrievalcited by 4
- 2020Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimationcited by 3
- 2023ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformerscited by 3
- 2023ViewFormer: View Set Attention for Multi-view 3D Shape Understandingcited by 1
- 2023Uncertainty-Aware Cross-Modal Transfer Network for Sketch-Based 3D Shape Retrievalcited by 1
Built on by
Not cited yet.