# Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database

arXiv: https://arxiv.org/abs/2207.01339 · HTML: https://mlsanity.com/paper/2207.01339/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 26 of 26)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation](https://mlsanity.com/paper/2001.00309/) (2020)
- [Scan2CAD: Learning CAD Model Alignment in RGB-D Scans](https://mlsanity.com/paper/1811.11187/) (2018)
- [PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors](https://mlsanity.com/paper/1808.10322/) (2018)
- [Fusion++: Volumetric Object-Level SLAM](https://mlsanity.com/paper/1808.08378/) (2018)
- [Triplet-Center Loss for Multi-View 3D Object Retrieval](https://mlsanity.com/paper/1803.06189/) (2018)
- [Joint Learning of 3D Shape Retrieval and Deformation](https://mlsanity.com/paper/2101.07889/) (2021)
- [End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans](https://mlsanity.com/paper/1906.04201/) (2019)
- [FutureMapping: The Computational Structure of Spatial AI Systems](https://mlsanity.com/paper/1803.11288/) (2018)
- [Marr Revisited: 2D-3D Alignment via Surface Normal Prediction](https://mlsanity.com/paper/1604.01347/) (2016)
- [RevealNet: Seeing Behind Objects in RGB-D Scans](https://mlsanity.com/paper/1904.12012/) (2019)
- [3D Local Features for Direct Pairwise Registration](https://mlsanity.com/paper/1904.04281/) (2019)
- [3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation](https://mlsanity.com/paper/1904.04427/) (2019)
- [Joint Embedding of 3D Scan and CAD Objects](https://mlsanity.com/paper/1908.06989/) (2019)
- [CAD-Deform: Deformable Fitting of CAD Models to 3D Scans](https://mlsanity.com/paper/2007.11965/) (2020)
- [Location Field Descriptors: Single Image 3D Model Retrieval in the Wild](https://mlsanity.com/paper/1908.02853/) (2019)
- [Fully Convolutional Geometric Features for Category-level Object Alignment](https://mlsanity.com/paper/2103.04494/) (2021)
- [CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration](https://mlsanity.com/paper/2103.06911/) (2021)

## Built on by (most cited first, 3 of 3)

- [LiteReality: Graphics-Ready 3D Scene Reconstruction from RGB-D Scans](https://mlsanity.com/paper/2507.02861/) (2025)
- [HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans](https://mlsanity.com/paper/2309.06107/) (2023)
- [From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation](https://mlsanity.com/paper/2302.03114/) (2023)

## Abstract (arXiv)

We present a new solution to the fine-grained retrieval of clean CAD models from a large-scale database in order to recover detailed object shape geometries for RGBD scans. Unlike previous work simply indexing into a moderately small database using an object shape descriptor and accepting the top retrieval result, we argue that in the case of a large-scale database a more accurate model may be found within a neighborhood of the descriptor. More importantly, we propose that the distinctiveness deficiency of shape descriptors at the instance level can be compensated by a geometry-based re-ranking of its neighborhood. Our approach first leverages the discriminative power of learned representations to distinguish between different categories of models and then uses a novel robust point set distance metric to re-rank the CAD neighborhood, enabling fine-grained retrieval in a large shape database. Evaluation on a real-world dataset shows that our geometry-based re-ranking is a conceptually simple but highly effective method that can lead to a significant improvement in retrieval accuracy compared to the state-of-the-art.