# Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds

arXiv: https://arxiv.org/abs/1802.01500 · HTML: https://mlsanity.com/paper/1802.01500/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2018.

## Built on by (most cited first, 30 of 33)

- [OpenMask3D: Open-Vocabulary 3D Instance Segmentation](https://mlsanity.com/paper/2306.13631/) (2023)
- [SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences](https://mlsanity.com/paper/2103.14898/) (2021)
- [Mix3D: Out-of-Context Data Augmentation for 3D Scenes](https://mlsanity.com/paper/2110.02210/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [PVT: Point-Voxel Transformer for Point Cloud Learning](https://mlsanity.com/paper/2108.06076/) (2021)
- [4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation](https://mlsanity.com/paper/2209.14858/) (2022)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation](https://mlsanity.com/paper/2303.11048/) (2023)
- [A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions](https://mlsanity.com/paper/2406.05785/) (2024)
- [3D Segmentation of Humans in Point Clouds with Synthetic Data](https://mlsanity.com/paper/2212.00786/) (2022)
- [Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook](https://mlsanity.com/paper/2311.02608/) (2023)
- [Rethinking Few-shot 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2403.00592/) (2024)
- [Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation](https://mlsanity.com/paper/2310.15676/) (2023)
- [Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2407.13642/) (2024)
- [FatNet: A Feature-attentive Network for 3D Point Cloud Processing](https://mlsanity.com/paper/2104.03427/) (2021)
- [GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2210.01558/) (2022)
- [UV-Based 3D Hand-Object Reconstruction with Grasp Optimization](https://mlsanity.com/paper/2211.13429/) (2022)
- [PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage](https://mlsanity.com/paper/2206.13082/) (2022)
- [Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image](https://mlsanity.com/paper/2210.05891/) (2022)
- [Full Point Encoding for Local Feature Aggregation in 3D Point Clouds](https://mlsanity.com/paper/2303.04458/) (2023)
- [Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data](https://mlsanity.com/paper/2203.00214/) (2022)
- [DisARM: Displacement Aware Relation Module for 3D Detection](https://mlsanity.com/paper/2203.01152/) (2022)
- [GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation](https://mlsanity.com/paper/2305.15213/) (2023)
- [Fusion of neural networks, for LIDAR-based evidential road mapping](https://mlsanity.com/paper/2102.03326/) (2021)
- [Generating Unrestricted 3D Adversarial Point Clouds](https://mlsanity.com/paper/2111.08973/) (2021)
- [DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds](https://mlsanity.com/paper/2303.04473/) (2023)
- [Robust 2D/3D Vehicle Parsing in CVIS](https://mlsanity.com/paper/2103.06432/) (2021)
- [Do as we do: Multiple Person Video-To-Video Transfer](https://mlsanity.com/paper/2104.04721/) (2021)
- [3DRM:Pair-wise relation module for 3D object detection](https://mlsanity.com/paper/2202.09721/) (2022)
- [Parametric Surface Constrained Upsampler Network for Point Cloud](https://mlsanity.com/paper/2303.08240/) (2023)

## Abstract (arXiv)

Deep learning approaches have made tremendous progress in the field of semantic segmentation over the past few years. However, most current approaches operate in the 2D image space. Direct semantic segmentation of unstructured 3D point clouds is still an open research problem. The recently proposed PointNet architecture presents an interesting step ahead in that it can operate on unstructured point clouds, achieving encouraging segmentation results. However, it subdivides the input points into a grid of blocks and processes each such block individually. In this paper, we investigate the question how such an architecture can be extended to incorporate larger-scale spatial context. We build upon PointNet and propose two extensions that enlarge the receptive field over the 3D scene. We evaluate the proposed strategies on challenging indoor and outdoor datasets and show improved results in both scenarios.