Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection

• URL: http://arxiv.org/abs/2308.15357v1
• Date: Tue, 29 Aug 2023 14:53:16 GMT
• Title: Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection
• Authors: Patrick Palmer, Martin Krueger, Richard Altendorfer, Torsten Bertram
• Abstract summary: One of high-resolution radar sensors, compared to lidar sensors, is the sparsity of the generated point cloud. This contribution analyzes limitations of accumulating radar point clouds on the View-of-Delft dataset. Experiments document an improved object detection performance by applying an ego-motion estimation and dynamic motion correction approach.
• Score: 0.1474723404975345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: New 3+1D high-resolution radar sensors are gaining importance for 3D object detection in the automotive domain due to their relative affordability and improved detection compared to classic low-resolution radar sensors. One limitation of high-resolution radar sensors, compared to lidar sensors, is the sparsity of the generated point cloud. This sparsity could be partially overcome by accumulating radar point clouds of subsequent time steps. This contribution analyzes limitations of accumulating radar point clouds on the View-of-Delft dataset. By employing different ego-motion estimation approaches, the dataset's inherent constraints, and possible solutions are analyzed. Additionally, a learning-based instance motion estimation approach is deployed to investigate the influence of dynamic motion on the accumulated point cloud for object detection. Experiments document an improved object detection performance by applying an ego-motion estimation and dynamic motion correction approach.

Related papers

• Multi-Object Tracking based on Imaging Radar 3D Object Detection [0.13499500088995461]
  This paper presents an approach for tracking surrounding traffic participants with a classical tracking algorithm. Learning based object detectors have been shown to work adequately on lidar and camera data, while learning based object detectors using standard radar data input have proven to be inferior. With the improvements to radar sensor technology in the form of imaging radars, the object detection performance on radar was greatly improved but is still limited compared to lidar sensors due to the sparsity of the radar point cloud. The tracking algorithm must overcome the limited detection quality while generating consistent tracks.
  arXiv  Detail & Related papers  (2024-06-03T05:46:23Z)
• Joint object detection and re-identification for 3D obstacle multi-camera systems [47.87501281561605]
  This research paper introduces a novel modification to an object detection network that uses camera and lidar information. It incorporates an additional branch designed for the task of re-identifying objects across adjacent cameras within the same vehicle. The results underscore the superiority of this method over traditional Non-Maximum Suppression (NMS) techniques.
  arXiv  Detail & Related papers  (2023-10-09T15:16:35Z)
• 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)
• DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds [55.755450273390004]
  Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D detectors is not explored mainly due to two reasons: (1) the onboard multi-object tracker cannot generate sufficient complete object trajectories, and (2) the motion state of objects poses an inevitable challenge for the object-centric refining stage. To tackle these problems, we propose a novel paradigm of offboard 3D object detection, named DetZero.
  arXiv  Detail & Related papers  (2023-06-09T16:42:00Z)
• Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization [13.727464375608765]
  Existing 3D object detectors tend to perform well on the point cloud regions closer to the LiDAR sensor as opposed to on regions that are farther away. We observe that there is a learning bias in detection models towards the dense objects near the sensor and show that the detection performance can be improved by simply manipulating the input point cloud density at different distance ranges.
  arXiv  Detail & Related papers  (2023-06-09T04:11:43Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds [50.54083964183614]
  It is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. We propose DMT, a Detector-free Motion prediction based 3D Tracking network that totally removes the usage of complicated 3D detectors.
  arXiv  Detail & Related papers  (2022-03-08T17:49:07Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)
• InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [65.47126868838836]
  We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
  arXiv  Detail & Related papers  (2020-07-16T18:27:08Z)
• Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds [22.850519892606716]
  We have developed a motion detector based on the shallow visual neural pathway of Drosophila. This detector is sensitive to the movement of objects and can well suppress background noise. An improved 3D object detection network is then used to estimate the point clouds of each proposal and efficiently generates the 3D bounding boxes and the object categories.
  arXiv  Detail & Related papers  (2020-05-06T10:04:23Z)

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.