RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar

• URL: http://arxiv.org/abs/2405.14014v3
• Date: Thu, 13 Jun 2024 16:51:50 GMT
• Title: RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar
• Authors: Fangqiang Ding, Xiangyu Wen, Lawrence Zhu, Yiming Li, Chris Xiaoxuan Lu,
• Abstract summary: 3D occupancy-based perception pipeline has significantly advanced autonomous driving. Current methods rely on LiDAR or camera inputs for 3D occupancy prediction. We introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction.
• Score: 15.776076554141687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D occupancy-based perception pipeline has significantly advanced autonomous driving by capturing detailed scene descriptions and demonstrating strong generalizability across various object categories and shapes. Current methods predominantly rely on LiDAR or camera inputs for 3D occupancy prediction. These methods are susceptible to adverse weather conditions, limiting the all-weather deployment of self-driving cars. To improve perception robustness, we leverage the recent advances in automotive radars and introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction. Our method, RadarOcc, circumvents the limitations of sparse radar point clouds by directly processing the 4D radar tensor, thus preserving essential scene details. RadarOcc innovatively addresses the challenges associated with the voluminous and noisy 4D radar data by employing Doppler bins descriptors, sidelobe-aware spatial sparsification, and range-wise self-attention mechanisms. To minimize the interpolation errors associated with direct coordinate transformations, we also devise a spherical-based feature encoding followed by spherical-to-Cartesian feature aggregation. We benchmark various baseline methods based on distinct modalities on the public K-Radar dataset. The results demonstrate RadarOcc's state-of-the-art performance in radar-based 3D occupancy prediction and promising results even when compared with LiDAR- or camera-based methods. Additionally, we present qualitative evidence of the superior performance of 4D radar in adverse weather conditions and explore the impact of key pipeline components through ablation studies.

Related papers

• 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)
• RadarPillars: Efficient Object Detection from 4D Radar Point Clouds [42.9356088038035]
  We present RadarPillars, a pillar-based object detection network. By decomposing radial velocity data, RadarPillars significantly outperform state-of-the-art detection results on the View-of-Delft dataset. This comes at a significantly reduced parameter count, surpassing existing methods in terms of efficiency and enabling real-time performance on edge devices.
  arXiv  Detail & Related papers  (2024-08-09T12:13:38Z)
• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar [28.640714690346353]
  CenterRadarNet is designed to facilitate high-resolution representation learning from 4D (Doppler-range-azimuth-ele) radar data. As a single-stage 3D object detector, CenterRadarNet infers the BEV object distribution confidence maps, corresponding 3D bounding box attributes, and appearance embedding for each pixel. In diverse driving scenarios, CenterRadarNet shows consistent, robust performance, emphasizing its wide applicability.
  arXiv  Detail & Related papers  (2023-11-02T17:36:40Z)
• MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection [15.925365473140479]
  4D radar is recognized for its resilience and cost-effectiveness under adverse weather conditions. Unlike LiDAR and cameras, radar remains unimpaired by harsh weather conditions. We propose a framework for developing radar-based 3D object detection for autonomous vehicles.
  arXiv  Detail & Related papers  (2023-10-25T06:10:07Z)
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion [74.84019379368807]
  We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
  arXiv  Detail & Related papers  (2023-07-31T09:53:50Z)
• K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions [9.705678194028895]
  KAIST-Radar is a novel large-scale object detection dataset and benchmark. It contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions. We provide auxiliary measurements from carefully calibrated high-resolution Lidars, surround stereo cameras, and RTK-GPS.
  arXiv  Detail & Related papers  (2022-06-16T13:39:21Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [52.59664614744447]
  We present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous velocity measurements.
  arXiv  Detail & Related papers  (2020-10-02T00:13:00Z)
• 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)

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.