# ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion

arXiv: https://arxiv.org/abs/2104.09587 · HTML: https://mlsanity.com/paper/2104.09587/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 30 of 30)

- [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)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation](https://mlsanity.com/paper/1802.05384/) (2018)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [Relation-Shape Convolutional Neural Network for Point Cloud Analysis](https://mlsanity.com/paper/1904.07601/) (2019)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis](https://mlsanity.com/paper/1612.00101/) (2016)
- [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020)
- [PF-Net: Point Fractal Network for 3D Point Cloud Completion](https://mlsanity.com/paper/2003.00410/) (2020)
- [Cascaded Refinement Network for Point Cloud Completion](https://mlsanity.com/paper/2004.03327/) (2020)
- [Point Cloud Completion by Skip-attention Network with Hierarchical Folding](https://mlsanity.com/paper/2005.03871/) (2020)
- [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016)
- [Detail Preserved Point Cloud Completion via Separated Feature Aggregation](https://mlsanity.com/paper/2007.02374/) (2020)
- [Triplet-Center Loss for Multi-View 3D Object Retrieval](https://mlsanity.com/paper/1803.06189/) (2018)
- [SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition](https://mlsanity.com/paper/2011.12430/) (2020)
- [RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion](https://mlsanity.com/paper/1904.12304/) (2019)
- [VConv-DAE: Deep Volumetric Shape Learning Without Object Labels](https://mlsanity.com/paper/1604.03755/) (2016)
- [Deformable Shape Completion with Graph Convolutional Autoencoders](https://mlsanity.com/paper/1712.00268/) (2017)
- [SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification](https://mlsanity.com/paper/2008.07358/) (2020)
- [LCD: Learned Cross-Domain Descriptors for 2D-3D Matching](https://mlsanity.com/paper/1911.09326/) (2019)
- [Point Set Voting for Partial Point Cloud Analysis](https://mlsanity.com/paper/2007.04537/) (2020)
- [VPC-Net: Completion of 3D Vehicles from MLS Point Clouds](https://mlsanity.com/paper/2008.03404/) (2020)
- [High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization](https://mlsanity.com/paper/1807.03407/) (2018)

## Built on by (most cited first, 28 of 28)

- [A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion](https://mlsanity.com/paper/2112.03530/) (2021)
- [SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer](https://mlsanity.com/paper/2207.10315/) (2022)
- [Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis](https://mlsanity.com/paper/2203.03311/) (2022)
- [ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer](https://mlsanity.com/paper/2302.14435/) (2023)
- [Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer](https://mlsanity.com/paper/2202.09367/) (2022)
- [LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints](https://mlsanity.com/paper/2203.16771/) (2022)
- [A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds](https://mlsanity.com/paper/2203.04232/) (2022)
- [CASSPR: Cross Attention Single Scan Place Recognition](https://mlsanity.com/paper/2211.12542/) (2022)
- [Learning Local Displacements for Point Cloud Completion](https://mlsanity.com/paper/2203.16600/) (2022)
- [CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion](https://mlsanity.com/paper/2211.13702/) (2022)
- [KTNet: Knowledge Transfer for Unpaired 3D Shape Completion](https://mlsanity.com/paper/2111.11976/) (2021)
- [3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion](https://mlsanity.com/paper/2404.07106/) (2024)
- [FSC: Few-point Shape Completion](https://mlsanity.com/paper/2403.07359/) (2024)
- [Multimodal Shape Completion via IMLE](https://mlsanity.com/paper/2106.16237/) (2021)
- [Temporal Point Cloud Completion with Pose Disturbance](https://mlsanity.com/paper/2202.03084/) (2022)
- [Shape Completion with Points in the Shadow](https://mlsanity.com/paper/2209.08345/) (2022)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- [SCP: Scene Completion Pre-training for 3D Object Detection](https://mlsanity.com/paper/2309.06199/) (2023)
- [Point cloud completion via structured feature maps using a feedback network](https://mlsanity.com/paper/2202.08583/) (2022)
- [Rethinking Data Input for Point Cloud Upsampling](https://mlsanity.com/paper/2407.04476/) (2024)
- [Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection](https://mlsanity.com/paper/2407.15771/) (2024)
- [PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation](https://mlsanity.com/paper/2210.03437/) (2022)
- [Unsupervised Point Cloud Completion through Unbalanced Optimal Transport](https://mlsanity.com/paper/2410.02671/) (2024)
- [Representation Learning of Point Cloud Upsampling in Global and Local Inputs](https://mlsanity.com/paper/2501.07076/) (2025)
- [SplAttN: Bridging 2D and 3D with Gaussian Soft Splatting and Attention for Point Cloud Completion](https://mlsanity.com/paper/2605.01466/) (2026)
- [Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net](https://mlsanity.com/paper/2407.08290/) (2024)
- [Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstruction](https://mlsanity.com/paper/2501.16716/) (2025)
- [Multi-modal Multi-task Pre-training for Improved Point Cloud Understanding](https://mlsanity.com/paper/2507.17533/) (2025)

## Abstract (arXiv)

We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net. Specifically, the Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a shared latent space, which can capture detailed shape prior. Then we design an iterative refinement unit to generate complete shapes with fine-grained details by integrating prior information. Experiments are conducted on the PCN dataset and the Completion3D benchmark, demonstrating the state-of-the-art performance of the proposed ASFM-Net. Our method achieves the 1st place in the leaderboard of Completion3D and outperforms existing methods with a large margin, about 12%. The codes and trained models are released publicly at https://github.com/Yan-Xia/ASFM-Net.