# PReP: Efficient context-based shape retrieval for missing parts

arXiv: https://arxiv.org/abs/2410.14245 · HTML: https://mlsanity.com/paper/2410.14245/

## 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 (most cited first, 30 of 33)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [Masked Autoencoders for Point Cloud Self-supervised Learning](https://mlsanity.com/paper/2203.06604/) (2022)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [Zero-Shot Composed Image Retrieval with Textual Inversion](https://mlsanity.com/paper/2303.15247/) (2023)
- [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020)
- [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://mlsanity.com/paper/1909.05235/) (2019)
- [Cross-Batch Memory for Embedding Learning](https://mlsanity.com/paper/1912.06798/) (2019)
- [Deep Metric Learning with Angular Loss](https://mlsanity.com/paper/1708.01682/) (2017)
- [Improved Embeddings with Easy Positive Triplet Mining](https://mlsanity.com/paper/1904.04370/) (2019)
- [Joint Learning of 3D Shape Retrieval and Deformation](https://mlsanity.com/paper/2101.07889/) (2021)
- [3D Part Assembly Generation with Instance Encoded Transformer](https://mlsanity.com/paper/2207.01779/) (2022)
- [Boosting vision transformers for image retrieval](https://mlsanity.com/paper/2210.11909/) (2022)
- [View N-gram Network for 3D Object Retrieval](https://mlsanity.com/paper/1908.01958/) (2019)
- [SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild](https://mlsanity.com/paper/2207.04945/) (2022)
- [Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning](https://mlsanity.com/paper/2010.12238/) (2020)
- [Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects](https://mlsanity.com/paper/2401.00405/) (2023)
- [Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly](https://mlsanity.com/paper/2303.01999/) (2023)
- [Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models](https://mlsanity.com/paper/2408.06145/) (2024)
- [3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval](https://mlsanity.com/paper/2210.15136/) (2022)
- [Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation](https://mlsanity.com/paper/2004.09048/) (2020)

## 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.