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

Pan, Yancheng; Gao, Biao; Mei, Jilin; Geng, Sibo; Li, Chengkun; Zhao, Huijing

doi:10.1109/iv47402.2020.9304596

Cited by 130 publications

(84 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we evaluate our approach on the public datasets SemanticKITTI [48], KITTI [49], and SemanticPOSS [50]. First, the SemanticKITTI dataset is used to verify the performance of SANet.…”

Section: Methodsmentioning

confidence: 99%

LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments

et al. 2021

View full text Add to dashboard Cite

Facing the realistic demands of the application environment of robots, the application of simultaneous localisation and mapping (SLAM) has gradually moved from static environments to complex dynamic environments, while traditional SLAM methods usually result in pose estimation deviations caused by errors in data association due to the interference of dynamic elements in the environment. This problem is effectively solved in the present study by proposing a SLAM approach based on light detection and ranging (LiDAR) under semantic constraints in dynamic environments. Four main modules are used for the projection of point cloud data, semantic segmentation, dynamic element screening, and semantic map construction. A LiDAR point cloud semantic segmentation network SANet based on a spatial attention mechanism is proposed, which significantly improves the real-time performance and accuracy of point cloud semantic segmentation. A dynamic element selection algorithm is designed and used with prior knowledge to significantly reduce the pose estimation deviations caused by SLAM dynamic elements. The results of experiments conducted on the public datasets SemanticKITTI, KITTI, and SemanticPOSS show that the accuracy and robustness of the proposed approach are significantly improved.

show abstract

Section: Methodsmentioning

confidence: 99%

LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The very recently published PandaSet (PandaSet, 2020) provides point-wise annotations of LiDAR point clouds with 42 classes focusing on objects on the road, such as traffic participants, barriers, and cones, and more fine-grained distinction between different vehicle types compared with our annotation. SemanticPOSS (Pan et al, 2020) also provides semantic annotation of point clouds with a focus on scenes with pedestrians captured in a campus environment. The classes are compatible with our classes and the authors provided labels in the same format as our annotation data.…”

Section: Related Workmentioning

confidence: 99%

“…The classes are compatible with our classes and the authors provided labels in the same format as our annotation data. Pan et al (2020) used our annotation tool presented in Section 3.1, but used tracking information to extract instances. NuScenes (Caesar et al, 2020) also recently added annotations for LiDAR point clouds with more diverse categories for different traffic participants.…”

Section: Related Workmentioning

confidence: 99%

Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

Behley

Garbade

Milioto

et al. 2021

The International Journal of Robotics Research

View full text Add to dashboard Cite

A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .

show abstract

“…We validate our approach on different scenarios getting state-of-the-art results. We use the Se-manticKitti (Behley et al, 2019) as the source domain and we adapt it to SemanticPoss (Pan et al, 2020), to Paris-Lille-3D (Roynard et al, 2018) and to a new collected and released dataset.…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions

Alonso¹,

Montesano²,

Murillo

2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

View full text Add to dashboard Cite

LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems, such as autonomous vehicles, during their decision making processes. Unfortunately, the annotation process for this task is very expensive. To overcome this, it is key to find models that generalize well or adapt to additional domains where labeled data is limited. This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space. Besides, we present a learningbased module to align the distribution of the semantic classes of the target domain to the source domain. Our approach achieves new state-of-the-art results on three different public datasets, which showcase adaptation to three different domains.

show abstract

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

Cited by 130 publications

References 15 publications

LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments

LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments

Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions

Contact Info

Product

Resources

About