# Skeleton-bridged Point Completion: From Global Inference to Local Adjustment

arXiv: https://arxiv.org/abs/2010.07428 · HTML: https://mlsanity.com/paper/2010.07428/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 23 of 23)

- [SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer](https://mlsanity.com/paper/2108.04444/) (2021)
- [Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion](https://mlsanity.com/paper/2111.12702/) (2021)
- [Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis](https://mlsanity.com/paper/2203.03311/) (2022)
- [Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer](https://mlsanity.com/paper/2202.09367/) (2022)
- [Voxel-based Network for Shape Completion by Leveraging Edge Generation](https://mlsanity.com/paper/2108.09936/) (2021)
- [LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints](https://mlsanity.com/paper/2203.16771/) (2022)
- [PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories](https://mlsanity.com/paper/2206.04916/) (2022)
- [Point Scene Understanding via Disentangled Instance Mesh Reconstruction](https://mlsanity.com/paper/2203.16832/) (2022)
- [ME-PCN: Point Completion Conditioned on Mask Emptiness](https://mlsanity.com/paper/2108.08187/) (2021)
- [Denoise and Contrast for Category Agnostic Shape Completion](https://mlsanity.com/paper/2103.16671/) (2021)
- [GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis](https://mlsanity.com/paper/2402.16994/) (2024)
- [SC-Diff: 3D Shape Completion with Latent Diffusion Models](https://mlsanity.com/paper/2403.12470/) (2024)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer](https://mlsanity.com/paper/2408.06596/) (2024)
- [Task-Aware Sampling Layer for Point-Wise Analysis](https://mlsanity.com/paper/2107.04291/) (2021)
- [Graph-Guided Deformation for Point Cloud Completion](https://mlsanity.com/paper/2112.01840/) (2021)
- [FSC: Few-point Shape Completion](https://mlsanity.com/paper/2403.07359/) (2024)
- [High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling](https://mlsanity.com/paper/2112.11271/) (2021)
- [Self-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism](https://mlsanity.com/paper/2203.10569/) (2022)
- [SPA-VAE: Similar-Parts-Assignment for Unsupervised 3D Point Cloud Generation](https://mlsanity.com/paper/2203.07825/) (2022)
- [PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network](https://mlsanity.com/paper/2403.01663/) (2024)
- [Diffusion-Driven Self-Supervised Learning for Shape Reconstruction and Pose Estimation](https://mlsanity.com/paper/2403.12728/) (2024)
- [DualGenerator: Information Interaction-based Generative Network for Point Cloud Completion](https://mlsanity.com/paper/2305.09132/) (2023)

## Abstract (arXiv)

Point completion refers to complete the missing geometries of objects from partial point clouds. Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points. However, real-world objects are usually with diverse topologies and surface details, which a latent feature may fail to represent to recover a clean and complete surface. To this end, we propose a skeleton-bridged point completion network (SK-PCN) for shape completion. Given a partial scan, our method first predicts its 3D skeleton to obtain the global structure, and completes the surface by learning displacements from skeletal points. We decouple the shape completion into structure estimation and surface reconstruction, which eases the learning difficulty and benefits our method to obtain on-surface details. Besides, considering the missing features during encoding input points, SK-PCN adopts a local adjustment strategy that merges the input point cloud to our predictions for surface refinement. Comparing with previous methods, our skeleton-bridged manner better supports point normal estimation to obtain the full surface mesh beyond point clouds. The qualitative and quantitative experiments on both point cloud and mesh completion show that our approach outperforms the existing methods on various object categories.