Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar

• URL: http://arxiv.org/abs/2308.05478v1
• Date: Thu, 10 Aug 2023 10:10:43 GMT
• Title: Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar
• Authors: Patrick Palmer and Martin Krueger and Richard Altendorfer and Ganesh Adam and Torsten Bertram
• Abstract summary: 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.
• Score: 0.7279730418361995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent developments and the beginning market introduction of high-resolution imaging 4D (3+1D) radar sensors have initialized deep learning-based radar perception research. We investigate deep learning-based models operating on radar point clouds for 3D object detection. 3D object detection on lidar point cloud data is a mature area of 3D vision. Many different architectures have been proposed, each with strengths and weaknesses. Due to similarities between 3D lidar point clouds and 3+1D radar point clouds, those existing 3D object detectors are a natural basis to start deep learning-based 3D object detection on radar data. Thus, the first step is to analyze the detection performance of the existing models on the new data modality and evaluate them in depth. In order to apply existing 3D point cloud object detectors developed for lidar point clouds to the radar domain, they need to be adapted first. While some detectors, such as PointPillars, have already been adapted to be applicable to radar data, we have adapted others, e.g., Voxel R-CNN, SECOND, PointRCNN, and PV-RCNN. To this end, we conduct a cross-model validation (evaluating a set of models on one particular data set) as well as a cross-data set validation (evaluating all models in the model set on several data sets). The high-resolution radar data used are the View-of-Delft and Astyx data sets. Finally, we evaluate several adaptations of the models and their training procedures. We also discuss major factors influencing the detection performance on radar data and propose possible solutions indicating potential future research avenues.

Related papers

• UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection [2.123197540438989]
  Many radar-vision fusion models treat radar as a sparse LiDAR, underutilizing radar-specific information. We propose the Radar Depth Lift-Splat-Shoot (RDL) module, which integrates radar-specific data into the depth prediction process. We also introduce a Unified Feature Fusion (UFF) approach that extracts BEV features across different modalities.
  arXiv  Detail & Related papers  (2024-09-23T06:57:27Z)
• GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling [7.90238039959534]
  Existing algorithms process radar data by projecting 3D points onto the image plane for pixel-level feature extraction. We propose GET-UP, leveraging attention-enhanced Graph Neural Networks (GNN) to exchange and aggregate both 2D and 3D information from radar data. We benchmark our proposed GET-UP on the nuScenes dataset, achieving state-of-the-art performance with a 15.3% and 14.7% improvement in MAE and RMSE over the previously best-performing model.
  arXiv  Detail & Related papers  (2024-09-02T14:15:09Z)
• VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
  monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
  arXiv  Detail & Related papers  (2024-04-15T03:12:12Z)
• 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
  3DiffTection is a state-of-the-art method for 3D object detection from single images. We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image. We further train the model on target data with detection supervision.
  arXiv  Detail & Related papers  (2023-11-07T23:46:41Z)
• 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)
• DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar [10.119506666546663]
  We introduce in this paper DeepPoint, a deep learning model that generates 3D objects in point cloud format. It takes as input the 2D depth images of an object generated by 3DRIMR's Stage 1, and outputs smooth and dense 3D point clouds of the object. Our experiments have demonstrated that this model significantly outperforms the original 3DRIMR and other standard techniques in reconstructing 3D objects.
  arXiv  Detail & Related papers  (2021-09-19T18:28:20Z)
• 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)
• ST3D: Self-training for Unsupervised Domain Adaptation on 3D ObjectDetection [78.71826145162092]
  We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised domain adaptation on 3D object detection from point clouds. Our ST3D achieves state-of-the-art performance on all evaluated datasets and even surpasses fully supervised results on KITTI 3D object detection benchmark.
  arXiv  Detail & Related papers  (2021-03-09T10:51:24Z)
• 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)
• Deep Learning on Radar Centric 3D Object Detection [4.822598110892847]
  We introduce a deep learning approach to 3D object detection with radar only. To overcome the lack of radar labeled data, we propose a novel way of making use of abundant LiDAR data.
  arXiv  Detail & Related papers  (2020-02-27T10:16:46Z)

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.