LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection

• URL: http://arxiv.org/abs/2301.12515v2
• Date: Tue, 5 Mar 2024 05:54:50 GMT
• Title: LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
• Authors: Jin Fang, Dingfu Zhou, Jingjing Zhao, Chenming Wu, Chulin Tang, Cheng-Zhong Xu and Liangjun Zhang
• Abstract summary: deep learning methods heavily rely on annotated data and often face domain generalization issues. LiDAR-CS dataset is the first dataset that addresses the sensor-related gaps in the domain of 3D object detection in real traffic.
• Score: 36.77084564823707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past few years, there has been remarkable progress in research on 3D point clouds and their use in autonomous driving scenarios has become widespread. However, deep learning methods heavily rely on annotated data and often face domain generalization issues. Unlike 2D images whose domains usually pertain to the texture information present in them, the features derived from a 3D point cloud are affected by the distribution of the points. The lack of a 3D domain adaptation benchmark leads to the common practice of training a model on one benchmark (e.g. Waymo) and then assessing it on another dataset (e.g. KITTI). This setting results in two distinct domain gaps: scenarios and sensors, making it difficult to analyze and evaluate the method accurately. To tackle this problem, this paper presents LiDAR Dataset with Cross Sensors (LiDAR-CS Dataset), which contains large-scale annotated LiDAR point cloud under six groups of different sensors but with the same corresponding scenarios, captured from hybrid realistic LiDAR simulator. To our knowledge, LiDAR-CS Dataset is the first dataset that addresses the sensor-related gaps in the domain of 3D object detection in real traffic. Furthermore, we evaluate and analyze the performance using various baseline detectors and demonstrated its potential applications. Project page: https://opendriving.github.io/lidar-cs.

Related papers

• LiOn-XA: Unsupervised Domain Adaptation via LiDAR-Only Cross-Modal Adversarial Training [61.26381389532653]
  LiOn-XA is an unsupervised domain adaptation (UDA) approach that combines LiDAR-Only Cross-Modal (X) learning with Adversarial training for 3D LiDAR point cloud semantic segmentation. Our experiments on 3 real-to-real adaptation scenarios demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-10-21T09:50:17Z)
• ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception [4.268591926288843]
  This paper provides a novel dataset, ParisLuco3D, specifically designed for cross-domain evaluation. Online benchmarks for LiDAR semantic segmentation, LiDAR object detection, and LiDAR tracking are provided.
  arXiv  Detail & Related papers  (2023-10-25T10:45:38Z)
• Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar [0.7279730418361995]
  High-resolution imaging 4D (3+1D) radar sensors have deep learning-based radar perception research. We investigate deep learning-based models operating on radar point clouds for 3D object detection.
  arXiv  Detail & Related papers  (2023-08-10T10:10:43Z)
• MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization [3.8243923744440926]
  3D object detection models trained on a source dataset with a specific point distribution have shown difficulties in generalizing to unseen datasets. We leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method. We show how we managed the mix of datasets during training and finally introduce a new cross-dataset augmentation method: cross-dataset object injection.
  arXiv  Detail & Related papers  (2023-08-02T08:20:00Z)
• SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud [125.9472454212909]
  We present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D) SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. Experiments show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label.
  arXiv  Detail & Related papers  (2022-12-06T09:32:44Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
  In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
  arXiv  Detail & Related papers  (2021-12-13T02:12:02Z)
• PC-DAN: Point Cloud based Deep Affinity Network for 3D Multi-Object Tracking (Accepted as an extended abstract in JRDB-ACT Workshop at CVPR21) [68.12101204123422]
  A point cloud is a dense compilation of spatial data in 3D coordinates. We propose a PointNet-based approach for 3D Multi-Object Tracking (MOT)
  arXiv  Detail & Related papers  (2021-06-03T05:36:39Z)
• H3D: Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and textured Meshes from UAV LiDAR and Multi-View-Stereo [4.263987603222371]
  This paper introduces a 3D dataset which is unique in three ways. It depicts the village of Hessigheim (Germany) henceforth referred to as H3D. It is designed for promoting research in the field of 3D data analysis on one hand and to evaluate and rank emerging approaches.
  arXiv  Detail & Related papers  (2021-02-10T09:33:48Z)
• Cirrus: A Long-range Bi-pattern LiDAR Dataset [35.87501129332217]
  We introduce Cirrus, a new long-range bi-pattern LiDAR public dataset for autonomous driving tasks. Our platform is equipped with a high-resolution video camera and a pair of LiDAR sensors with a 250-meter effective range. In Cirrus, eight categories of objects are exhaustively annotated in the LiDAR point clouds for the entire effective range.
  arXiv  Detail & Related papers  (2020-12-05T03:18:31Z)
• Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds [49.47017280475232]
  We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds. We take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors.
  arXiv  Detail & Related papers  (2020-07-16T17:42:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.