# Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors

arXiv: https://arxiv.org/abs/2001.04803 · HTML: https://mlsanity.com/paper/2001.04803/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds](https://mlsanity.com/paper/2108.09169/) (2021)
- [Taxonomy Completion via Triplet Matching Network](https://mlsanity.com/paper/2101.01896/) (2021)
- [PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling](https://mlsanity.com/paper/2204.10750/) (2022)
- [Masked Surfel Prediction for Self-Supervised Point Cloud Learning](https://mlsanity.com/paper/2207.03111/) (2022)
- [3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image](https://mlsanity.com/paper/2107.11010/) (2021)
- [A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds](https://mlsanity.com/paper/2104.05164/) (2021)
- [TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation](https://mlsanity.com/paper/2412.20911/) (2024)
- [Prototype-Aware Heterogeneous Task for Point Cloud Completion](https://mlsanity.com/paper/2209.01733/) (2022)
- [SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image](https://mlsanity.com/paper/2305.12646/) (2023)
- [Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation](https://mlsanity.com/paper/2409.06956/) (2024)
- [Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition](https://mlsanity.com/paper/2506.21165/) (2025)

## Abstract (arXiv)

Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner. However, very few explore geometric properties revealing local surface manifolds embedded in 3D Euclidean space to discriminate semantic classes or object parts as additional supervision signals. This paper is the first attempt to propose a unique multi-task geometric learning network to improve semantic analysis by auxiliary geometric learning with local shape properties, which can be either generated via physical computation from point clouds themselves as self-supervision signals or provided as privileged information. Owing to explicitly encoding local shape manifolds in favor of semantic analysis, the proposed geometric self-supervised and privileged learning algorithms can achieve superior performance to their backbone baselines and other state-of-the-art methods, which are verified in the experiments on the popular benchmarks.