# Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning

arXiv: https://arxiv.org/abs/2105.10203 · HTML: https://mlsanity.com/paper/2105.10203/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 44 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 30 of 35)

- [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)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://mlsanity.com/paper/2003.00492/) (2020)
- [SEGCloud: Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1710.07563/) (2017)
- [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017)
- [Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling](https://mlsanity.com/paper/1904.03375/) (2019)
- [3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation](https://mlsanity.com/paper/1803.10409/) (2018)
- [ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics](https://mlsanity.com/paper/1908.06295/) (2019)
- [Associatively Segmenting Instances and Semantics in Point Clouds](https://mlsanity.com/paper/1902.09852/) (2019)
- [Recurrent Slice Networks for 3D Segmentation of Point Clouds](https://mlsanity.com/paper/1802.04402/) (2018)
- [Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/1909.10469/) (2019)
- [JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields](https://mlsanity.com/paper/1904.00699/) (2019)
- [JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds](https://mlsanity.com/paper/2007.06888/) (2020)
- [Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds](https://mlsanity.com/paper/1909.09287/) (2019)
- [FPConv: Learning Local Flattening for Point Convolution](https://mlsanity.com/paper/2002.10701/) (2020)
- [Region Mutual Information Loss for Semantic Segmentation](https://mlsanity.com/paper/1910.12037/) (2019)
- [TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes](https://mlsanity.com/paper/1812.00020/) (2018)
- [DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes](https://mlsanity.com/paper/2004.01002/) (2020)
- [Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation](https://mlsanity.com/paper/2003.06233/) (2020)
- [Multi-Scale Structure-Aware Network for Human Pose Estimation](https://mlsanity.com/paper/1803.09894/) (2018)
- [JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1912.09654/) (2019)

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

- [Mix3D: Out-of-Context Data Augmentation for 3D Scenes](https://mlsanity.com/paper/2110.02210/) (2021)
- [Contrastive Boundary Learning for Point Cloud Segmentation](https://mlsanity.com/paper/2203.05272/) (2022)
- [CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2307.10316/) (2023)
- [SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation](https://mlsanity.com/paper/2205.13490/) (2022)
- [HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor](https://mlsanity.com/paper/2107.03180/) (2021)
- [Building a Strong Pre-Training Baseline for Universal 3D Large-Scale Perception](https://mlsanity.com/paper/2405.07201/) (2024)
- [Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2308.09314/) (2023)
- [Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization](https://mlsanity.com/paper/2401.06975/) (2024)
- [Projection-based Point Convolution for Efficient Point Cloud Segmentation](https://mlsanity.com/paper/2202.01991/) (2022)
- [Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene](https://mlsanity.com/paper/2301.12972/) (2023)
- [Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes](https://mlsanity.com/paper/2503.10055/) (2025)
- [SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2301.06869/) (2023)
- [Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion](https://mlsanity.com/paper/2111.08434/) (2021)
- [3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning](https://mlsanity.com/paper/2201.02198/) (2022)
- [Benchmarking Deep Learning Architectures for Urban Vegetation Point Cloud Semantic Segmentation from MLS](https://mlsanity.com/paper/2306.10274/) (2023)
- [Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment](https://mlsanity.com/paper/2312.00663/) (2023)
- [YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos](https://mlsanity.com/paper/2506.18266/) (2025)
- [Ambiguity-aware Point Cloud Segmentation by Adaptive Margin Contrastive Learning](https://mlsanity.com/paper/2507.06592/) (2025)
- [Human Motion Synthesis in 3D Scenes via Unified Scene Semantic Occupancy](https://mlsanity.com/paper/2511.07819/) (2025)
- [Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution](https://mlsanity.com/paper/2207.01181/) (2022)
- [DEMOS: Dynamic Environment Motion Synthesis in 3D Scenes via Local Spherical-BEV Perception](https://mlsanity.com/paper/2403.01740/) (2024)
- [World2Minecraft: Occupancy-Driven Simulated Scenes Construction](https://mlsanity.com/paper/2604.27578/) (2026)

## Abstract (arXiv)

Hidden features in neural network usually fail to learn informative representation for 3D segmentation as supervisions are only given on output prediction, while this can be solved by omni-scale supervision on intermediate layers. In this paper, we bring the first omni-scale supervision method to point cloud segmentation via the proposed gradual Receptive Field Component Reasoning (RFCR), where target Receptive Field Component Codes (RFCCs) are designed to record categories within receptive fields for hidden units in the encoder. Then, target RFCCs will supervise the decoder to gradually infer the RFCCs in a coarse-to-fine categories reasoning manner, and finally obtain the semantic labels. Because many hidden features are inactive with tiny magnitude and make minor contributions to RFCC prediction, we propose a Feature Densification with a centrifugal potential to obtain more unambiguous features, and it is in effect equivalent to entropy regularization over features. More active features can further unleash the potential of our omni-supervision method. We embed our method into four prevailing backbones and test on three challenging benchmarks. Our method can significantly improve the backbones in all three datasets. Specifically, our method brings new state-of-the-art performances for S3DIS as well as Semantic3D and ranks the 1st in the ScanNet benchmark among all the point-based methods. Code will be publicly available at https://github.com/azuki-miho/RFCR.