# SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance

arXiv: https://arxiv.org/abs/2503.18007 · HTML: https://mlsanity.com/paper/2503.18007/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 38 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 28 of 28)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020)
- [SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer](https://mlsanity.com/paper/2108.04444/) (2021)
- [PF-Net: Point Fractal Network for 3D Point Cloud Completion](https://mlsanity.com/paper/2003.00410/) (2020)
- [Morphing and Sampling Network for Dense Point Cloud Completion](https://mlsanity.com/paper/1912.00280/) (2019)
- [Cascaded Refinement Network for Point Cloud Completion](https://mlsanity.com/paper/2004.03327/) (2020)
- [Variational Relational Point Completion Network](https://mlsanity.com/paper/2104.10154/) (2021)
- [PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths](https://mlsanity.com/paper/2012.03408/) (2020)
- [SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer](https://mlsanity.com/paper/2207.10315/) (2022)
- [LEAP: Liberate Sparse-view 3D Modeling from Camera Poses](https://mlsanity.com/paper/2310.01410/) (2023)
- [3DN: 3D Deformation Network](https://mlsanity.com/paper/1903.03322/) (2019)
- [ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer](https://mlsanity.com/paper/2302.14435/) (2023)
- [SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator](https://mlsanity.com/paper/2307.08492/) (2023)
- [Voxel-based Network for Shape Completion by Leveraging Edge Generation](https://mlsanity.com/paper/2108.09936/) (2021)
- [CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers](https://mlsanity.com/paper/2401.01552/) (2024)
- [NeRD: Neural 3D Reflection Symmetry Detector](https://mlsanity.com/paper/2105.03211/) (2021)
- [Towards realistic symmetry-based completion of previously unseen point clouds](https://mlsanity.com/paper/2201.01858/) (2022)
- [Refinement of Predicted Missing Parts Enhance Point Cloud Completion](https://mlsanity.com/paper/2010.04278/) (2020)
- [Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map](https://mlsanity.com/paper/2308.13245/) (2023)

## Built on by (most cited first, 15 of 15)

- [Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets](https://mlsanity.com/paper/2509.21245/) (2025)
- [DiffPCN: Latent Diffusion Model Based on Multi-view Depth Images for Point Cloud Completion](https://mlsanity.com/paper/2509.23723/) (2025)
- [Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspective](https://mlsanity.com/paper/2511.12170/) (2025)
- [PoseMaster: A Unified 3D Native Framework for Stylized Pose Generation](https://mlsanity.com/paper/2506.21076/) (2025)
- [DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation](https://mlsanity.com/paper/2607.02220/) (2026)
- [Simba: Towards High-Fidelity and Geometrically-Consistent Point Cloud Completion via Transformation Diffusion](https://mlsanity.com/paper/2511.16161/) (2025)
- [HeartFormer: Semantic-Aware Dual-Structure Transformers for 3D Four-Chamber Cardiac Point Cloud Reconstruction](https://mlsanity.com/paper/2512.00264/) (2025)
- [SplAttN: Bridging 2D and 3D with Gaussian Soft Splatting and Attention for Point Cloud Completion](https://mlsanity.com/paper/2605.01466/) (2026)
- [SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation](https://mlsanity.com/paper/2606.04108/) (2026)
- [Step Back to Move Forward: Reflection-Aware Preference Optimization for Visual Generation](https://mlsanity.com/paper/2609.04282/) (2026)
- [Joint Alignment and Distillation for Video Generation via Sample-Guided Distribution Matching](https://mlsanity.com/paper/2609.04283/) (2026)
- [SCORP: Scene-Consistent Object Refinement via Proxy Generation and Tuning](https://mlsanity.com/paper/2506.23835/) (2025)
- [Manifold-Aware Point Cloud Completion via Geodesic-Attentive Hierarchical Feature Learning](https://mlsanity.com/paper/2512.05710/) (2025)
- [Unified Primitive Proxies for Structured Shape Completion](https://mlsanity.com/paper/2601.00759/) (2026)
- [SUMI: Scalable Unified Model for 3D Point Cloud Inference](https://mlsanity.com/paper/2608.08115/) (2026)

## Abstract (arXiv)

Point cloud completion aims to recover a complete point shape from a partial point cloud. Although existing methods can form satisfactory point clouds in global completeness, they often lose the original geometry details and face the problem of geometric inconsistency between existing point clouds and reconstructed missing parts. To tackle this problem, we introduce SymmCompletion, a highly effective completion method based on symmetry guidance. Our method comprises two primary components: a Local Symmetry Transformation Network (LSTNet) and a Symmetry-Guidance Transformer (SGFormer). First, LSTNet efficiently estimates point-wise local symmetry transformation to transform key geometries of partial inputs into missing regions, thereby generating geometry-align partial-missing pairs and initial point clouds. Second, SGFormer leverages the geometric features of partial-missing pairs as the explicit symmetric guidance that can constrain the refinement process for initial point clouds. As a result, SGFormer can exploit provided priors to form high-fidelity and geometry-consistency final point clouds. Qualitative and quantitative evaluations on several benchmark datasets demonstrate that our method outperforms state-of-the-art completion networks.