# SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances

arXiv: https://arxiv.org/abs/2002.09147 · HTML: https://mlsanity.com/paper/2002.09147/

## Key facts

- Cited by 123 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 30 of 123)

- [OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception](https://mlsanity.com/paper/2303.03991/) (2023)
- [Segment Any Point Cloud Sequences by Distilling Vision Foundation Models](https://mlsanity.com/paper/2306.09347/) (2023)
- [SCPNet: Semantic Scene Completion on Point Cloud](https://mlsanity.com/paper/2303.06884/) (2023)
- [Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation](https://mlsanity.com/paper/2107.05399/) (2021)
- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds](https://mlsanity.com/paper/2208.00223/) (2022)
- [ALSO: Automotive Lidar Self-supervision by Occupancy estimation](https://mlsanity.com/paper/2212.05867/) (2022)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Learning 3D Semantic Segmentation with only 2D Image Supervision](https://mlsanity.com/paper/2110.11325/) (2021)
- [CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2207.12691/) (2022)
- [CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation](https://mlsanity.com/paper/2207.09778/) (2022)
- [STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset](https://mlsanity.com/paper/2203.09065/) (2022)
- [FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation](https://mlsanity.com/paper/2312.04484/) (2023)
- [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://mlsanity.com/paper/2201.04494/) (2022)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation](https://mlsanity.com/paper/2404.11958/) (2024)
- [MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments](https://mlsanity.com/paper/2203.07060/) (2022)
- [GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network](https://mlsanity.com/paper/2108.08401/) (2021)
- [A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective](https://mlsanity.com/paper/2405.05173/) (2024)
- [4D Contrastive Superflows are Dense 3D Representation Learners](https://mlsanity.com/paper/2407.06190/) (2024)
- [LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR](https://mlsanity.com/paper/2302.14052/) (2023)
- [OccMamba: Semantic Occupancy Prediction with State Space Models](https://mlsanity.com/paper/2408.09859/) (2024)
- [Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping](https://mlsanity.com/paper/2111.11348/) (2021)
- [Domain generalization of 3D semantic segmentation in autonomous driving](https://mlsanity.com/paper/2212.04245/) (2022)
- [BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds](https://mlsanity.com/paper/2310.17281/) (2023)
- [Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities](https://mlsanity.com/paper/2401.08045/) (2024)
- [Multi-Space Alignments Towards Universal LiDAR Segmentation](https://mlsanity.com/paper/2405.01538/) (2024)
- [Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation](https://mlsanity.com/paper/2202.13377/) (2022)
- [Spatiotemporal Self-supervised Learning for Point Clouds in the Wild](https://mlsanity.com/paper/2303.16235/) (2023)
- [MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding](https://mlsanity.com/paper/2107.00346/) (2021)

## Abstract (arXiv)

3D semantic segmentation is one of the key tasks for autonomous driving system. Recently, deep learning models for 3D semantic segmentation task have been widely researched, but they usually require large amounts of training data. However, the present datasets for 3D semantic segmentation are lack of point-wise annotation, diversiform scenes and dynamic objects. In this paper, we propose the SemanticPOSS dataset, which contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances. The data is collected in Peking University and uses the same data format as SemanticKITTI. In addition, we evaluate several typical 3D semantic segmentation models on our SemanticPOSS dataset. Experimental results show that SemanticPOSS can help to improve the prediction accuracy of dynamic objects as people, car in some degree. SemanticPOSS will be published at urlwww.poss.pku.edu.cn.