# Point Set Voting for Partial Point Cloud Analysis

arXiv: https://arxiv.org/abs/2007.04537 · HTML: https://mlsanity.com/paper/2007.04537/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer](https://mlsanity.com/paper/2108.04444/) (2021)
- [PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths](https://mlsanity.com/paper/2202.09507/) (2022)
- [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)
- [ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion](https://mlsanity.com/paper/2104.09587/) (2021)
- [UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.24381/) (2025)
- [SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems](https://mlsanity.com/paper/2506.07564/) (2025)
- [Learning Local Displacements for Point Cloud Completion](https://mlsanity.com/paper/2203.16600/) (2022)
- [Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning](https://mlsanity.com/paper/2112.05213/) (2021)
- [EqDrive: Efficient Equivariant Motion Forecasting with Multi-Modality for Autonomous Driving](https://mlsanity.com/paper/2310.17540/) (2023)
- [SoftPool++: An Encoder-Decoder Network for Point Cloud Completion](https://mlsanity.com/paper/2205.03899/) (2022)
- [Equivariant Map and Agent Geometry for Autonomous Driving Motion Prediction](https://mlsanity.com/paper/2310.13922/) (2023)
- [EPiC: Ensemble of Partial Point Clouds for Robust Classification](https://mlsanity.com/paper/2303.11419/) (2023)
- [Hyperspherical Embedding for Point Cloud Completion](https://mlsanity.com/paper/2307.05634/) (2023)
- [Novel View Synthesis from a Single RGBD Image for Indoor Scenes](https://mlsanity.com/paper/2311.01065/) (2023)
- [RDD: Retrieval-Based Demonstration Decomposer for Planner Alignment in Long-Horizon Tasks](https://mlsanity.com/paper/2510.14968/) (2025)
- [Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation](https://mlsanity.com/paper/2203.09772/) (2022)
- [Prototype-Aware Heterogeneous Task for Point Cloud Completion](https://mlsanity.com/paper/2209.01733/) (2022)
- [DualGenerator: Information Interaction-based Generative Network for Point Cloud Completion](https://mlsanity.com/paper/2305.09132/) (2023)
- [A Strong View-Free Baseline Approach for Single-View Image Guided Point Cloud Completion](https://mlsanity.com/paper/2506.15747/) (2025)
- [Point-Selection Fine-Tuning Framework for Robust Point Cloud Classification](https://mlsanity.com/paper/2607.19711/) (2026)

## Abstract (arXiv)

The continual improvement of 3D sensors has driven the development of algorithms to perform point cloud analysis. In fact, techniques for point cloud classification and segmentation have in recent years achieved incredible performance driven in part by leveraging large synthetic datasets. Unfortunately these same state-of-the-art approaches perform poorly when applied to incomplete point clouds. This limitation of existing algorithms is particularly concerning since point clouds generated by 3D sensors in the real world are usually incomplete due to perspective view or occlusion by other objects. This paper proposes a general model for partial point clouds analysis wherein the latent feature encoding a complete point clouds is inferred by applying a local point set voting strategy. In particular, each local point set constructs a vote that corresponds to a distribution in the latent space, and the optimal latent feature is the one with the highest probability. This approach ensures that any subsequent point cloud analysis is robust to partial observation while simultaneously guaranteeing that the proposed model is able to output multiple possible results. This paper illustrates that this proposed method achieves state-of-the-art performance on shape classification, part segmentation and point cloud completion.