InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data

• URL: http://arxiv.org/abs/2303.03909v1
• Date: Tue, 7 Mar 2023 14:12:52 GMT
• Title: InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
• Authors: Neng Wang, Chenghao Shi, Ruibin Guo, Huimin Lu, Zhiqiang Zheng, Xieyuanli Chen
• Abstract summary: We propose a novel network that addresses the challenge of segmenting moving objects in 3D LiDAR scans. Our method exploits a sequence of point clouds as input and quantifies them into 4D voxels. We use 4D sparse convolutions to extract motion features from the 4D voxels and inject them into the current scan.
• Score: 13.196031553445117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a novel network that addresses the challenge of segmenting moving objects in 3D LiDAR scans. Our approach not only predicts point-wise moving labels but also detects instance information of main traffic participants. Such a design helps determine which instances are actually moving and which ones are temporarily static in the current scene. Our method exploits a sequence of point clouds as input and quantifies them into 4D voxels. We use 4D sparse convolutions to extract motion features from the 4D voxels and inject them into the current scan. Then, we extract spatio-temporal features from the current scan for instance detection and feature fusion. Finally, we design an upsample fusion module to output point-wise labels by fusing the spatio-temporal features and predicted instance information. We evaluated our approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better moving object segmentation performance compared to state-of-the-art methods, demonstrating the effectiveness of our approach in integrating instance information for moving object segmentation. Furthermore, our method shows superior performance on the Apollo dataset with a pre-trained model on SemanticKITTI, indicating that our method generalizes well in different scenes.The code and pre-trained models of our method will be released at https://github.com/nubot-nudt/InsMOS.

Related papers

• SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics [0.7373617024876725]
  In autonomous driving, 3D object detection provides more precise information for downstream tasks, including path planning and motion estimation. We propose SeSame: a method aimed at enhancing semantic information in existing LiDAR-only based 3D object detection. Experiments demonstrate the effectiveness of our method with performance improvements on the KITTI object detection benchmark.
  arXiv  Detail & Related papers  (2024-03-11T08:17:56Z)
• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion [110.84357383258818]
  We propose a novel approach to lift 2D segments to 3D and fuse them by means of a neural field representation. The core of our approach is a slow-fast clustering objective function, which is scalable and well-suited for scenes with a large number of objects. Our approach outperforms the state-of-the-art on challenging scenes from the ScanNet, Hypersim, and Replica datasets.
  arXiv  Detail & Related papers  (2023-06-07T17:57:45Z)
• Event-Free Moving Object Segmentation from Moving Ego Vehicle [88.33470650615162]
  Moving object segmentation (MOS) in dynamic scenes is an important, challenging, but under-explored research topic for autonomous driving. Most segmentation methods leverage motion cues obtained from optical flow maps. We propose to exploit event cameras for better video understanding, which provide rich motion cues without relying on optical flow.
  arXiv  Detail & Related papers  (2023-04-28T23:43:10Z)
• Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection [50.959453059206446]
  This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. We propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective.
  arXiv  Detail & Related papers  (2023-04-24T17:59:05Z)
• Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions [33.538055872850514]
  We tackle the problem of distinguishing 3D LiDAR points that belong to currently moving objects, like walking pedestrians or driving cars, from points that are obtained from non-moving objects, like walls but also parked cars. Our approach takes a sequence of observed LiDAR scans and turns them into a voxelized sparse 4D point cloud. We apply computationally efficient sparse 4D convolutions to jointly extract spatial and temporal features and predict moving object confidence scores for all points in the sequence.
  arXiv  Detail & Related papers  (2022-06-08T18:51:14Z)
• "What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences [27.915309216800125]
  We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. We propose a single, end-to-end trainable architecture which jointly incorporates motion cues and semantic knowledge. Our method neither depends on any visual registration of a kinematic robot or 3D object models, nor on precise hand-eye calibration or any additional sensor data.
  arXiv  Detail & Related papers  (2020-11-06T10:55:28Z)
• AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points [92.91569287889203]
  We present a novel, label-free algorithm, AutoTrajectory, for trajectory extraction and prediction. To better capture the moving objects in videos, we introduce dynamic points. We aggregate dynamic points to instance points, which stand for moving objects such as pedestrians in videos.
  arXiv  Detail & Related papers  (2020-07-11T08:43:34Z)
• DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
  We propose a fast single-stage 3D object detection method for LIDAR data. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
  arXiv  Detail & Related papers  (2020-04-02T17:48:50Z)

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.