# Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

arXiv: https://arxiv.org/abs/2008.01550 · HTML: https://mlsanity.com/paper/2008.01550/

## Key facts

- Cited by 112 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 30 of 112)

- [SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving](https://mlsanity.com/paper/2303.09551/) (2023)
- [Uni3D: Exploring Unified 3D Representation at Scale](https://mlsanity.com/paper/2310.06773/) (2023)
- [(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network](https://mlsanity.com/paper/2102.04530/) (2021)
- [2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds](https://mlsanity.com/paper/2207.04397/) (2022)
- [RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2103.10039/) (2021)
- [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://mlsanity.com/paper/2103.15297/) (2021)
- [FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection](https://mlsanity.com/paper/2106.12449/) (2021)
- [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022)
- [Segment Anything in 3D with Radiance Fields](https://mlsanity.com/paper/2304.12308/) (2023)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception](https://mlsanity.com/paper/2109.05441/) (2021)
- [CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2207.12691/) (2022)
- [NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields](https://mlsanity.com/paper/2304.14811/) (2023)
- [Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models](https://mlsanity.com/paper/2106.04180/) (2021)
- [Camera-based 3D Semantic Scene Completion with Sparse Guidance Network](https://mlsanity.com/paper/2312.05752/) (2023)
- [Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions](https://mlsanity.com/paper/2103.08852/) (2021)
- [GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network](https://mlsanity.com/paper/2108.08401/) (2021)
- [Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds](https://mlsanity.com/paper/2109.11453/) (2021)
- [LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation](https://mlsanity.com/paper/2108.07511/) (2021)
- [FastLGS: Speeding up Language Embedded Gaussians with Feature Grid Mapping](https://mlsanity.com/paper/2406.01916/) (2024)
- [OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2403.01644/) (2024)
- [Fully Sparse Fusion for 3D Object Detection](https://mlsanity.com/paper/2304.12310/) (2023)
- [Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset](https://mlsanity.com/paper/2309.13549/) (2023)
- [RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation](https://mlsanity.com/paper/2312.11829/) (2023)
- [Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation](https://mlsanity.com/paper/2207.01878/) (2022)
- [DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation](https://mlsanity.com/paper/2108.04023/) (2021)
- [MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds](https://mlsanity.com/paper/2307.09316/) (2023)
- [PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars](https://mlsanity.com/paper/2106.07545/) (2021)
- [EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder](https://mlsanity.com/paper/2212.04098/) (2022)
- [Benchmarking the Robustness of LiDAR Semantic Segmentation Models](https://mlsanity.com/paper/2301.00970/) (2023)
- [Occam's LGS: An Efficient Approach for Language Gaussian Splatting](https://mlsanity.com/paper/2412.01807/) (2024)

## Abstract (arXiv)

State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. The projection methods includes spherical projection, bird-eye view projection, etc. Although this process makes the point cloud suitable for the 2D CNN-based networks, it inevitably alters and abandons the 3D topology and geometric relations. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. In this work, we first perform an in-depth analysis for different representations and backbones in 2D and 3D spaces, and reveal the effectiveness of 3D representations and networks on LiDAR segmentation. Then, we develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds. Moreover, a dimension-decomposition based context modeling module is introduced to explore the high-rank context information in point clouds in a progressive manner. We evaluate the proposed model on a large-scale driving-scene dataset, i.e. SematicKITTI. Our method achieves state-of-the-art performance and outperforms existing methods by 6% in terms of mIoU.