# Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar

arXiv: https://arxiv.org/abs/2205.02111 · HTML: https://mlsanity.com/paper/2205.02111/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [CNN based Road User Detection using the 3D Radar Cube](https://mlsanity.com/paper/2004.12165/) (2020)
- [2D Car Detection in Radar Data with PointNets](https://mlsanity.com/paper/1904.08414/) (2019)
- [Pointillism: Accurate 3D bounding box estimation with multi-radars](https://mlsanity.com/paper/2203.04440/) (2022)
- [Deep Radar Detector](https://mlsanity.com/paper/1906.12187/) (2019)
- [Radar Voxel Fusion for 3D Object Detection](https://mlsanity.com/paper/2106.14087/) (2021)
- [Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?](https://mlsanity.com/paper/2006.05485/) (2020)
- [DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections](https://mlsanity.com/paper/2010.09273/) (2020)
- [Deep Learning on Radar Centric 3D Object Detection](https://mlsanity.com/paper/2003.00851/) (2020)

## Built on by (most cited first, 30 of 33)

- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection](https://mlsanity.com/paper/2403.16440/) (2024)
- [CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer](https://mlsanity.com/paper/2209.06535/) (2022)
- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception](https://mlsanity.com/paper/2403.07746/) (2024)
- [RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features](https://mlsanity.com/paper/2403.05061/) (2024)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection](https://mlsanity.com/paper/2411.03013/) (2024)
- [DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars](https://mlsanity.com/paper/2209.12729/) (2022)
- [LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection](https://mlsanity.com/paper/2402.11735/) (2024)
- [RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network](https://mlsanity.com/paper/2409.04979/) (2024)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2411.19860/) (2024)
- [Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving](https://mlsanity.com/paper/2407.12803/) (2024)
- [RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection](https://mlsanity.com/paper/2412.12799/) (2024)
- [Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks](https://mlsanity.com/paper/2305.15836/) (2023)
- [Radar Spectra-Language Model for Automotive Scene Parsing](https://mlsanity.com/paper/2406.02158/) (2024)
- [DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection](https://mlsanity.com/paper/2508.12330/) (2025)
- [Automotive Object Detection via Learning Sparse Events by Spiking Neurons](https://mlsanity.com/paper/2307.12900/) (2023)
- [ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/2309.03734/) (2023)
- [SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data](https://mlsanity.com/paper/2406.10600/) (2024)
- [CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving](https://mlsanity.com/paper/2508.13485/) (2025)
- [Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion](https://mlsanity.com/paper/2307.03623/) (2023)
- [AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection](https://mlsanity.com/paper/2504.00559/) (2025)
- [RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion](https://mlsanity.com/paper/2305.15883/) (2023)
- [Exploiting Sparsity in Automotive Radar Object Detection Networks](https://mlsanity.com/paper/2308.07748/) (2023)
- [Reinforcement Learning for SAR View Angle Inversion with Differentiable SAR Renderer](https://mlsanity.com/paper/2401.01165/) (2024)
- [Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection](https://mlsanity.com/paper/2402.08427/) (2024)
- [PAN: Pillars-Attention-Based Network for 3D Object Detection](https://mlsanity.com/paper/2509.15935/) (2025)
- [Histogram-based Deep Learning for Automotive Radar](https://mlsanity.com/paper/2303.02975/) (2023)

## Abstract (arXiv)

This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks. Purely grid-based detection models operate on a bird's-eye-view (BEV) projection of the input point cloud. These approaches suffer from a loss of detailed information through the discrete grid resolution. This applies in particular to radar object detection, where relatively coarse grid resolutions are commonly used to account for the sparsity of radar point clouds. In contrast, point-based models are not affected by this problem as they process point clouds without discretization. However, they generally exhibit worse detection performances than grid-based methods. We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering. This has significant benefits for a subsequent grid-based convolutional detection backbone. In experiments on the public nuScenes dataset our hybrid architecture achieves improvements in terms of detection performance (19.7% higher mAP for car class than next-best radar-only submission) and orientation estimates (11.5% relative orientation improvement) over networks from previous literature.