# Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection

arXiv: https://arxiv.org/abs/2303.06342 · HTML: https://mlsanity.com/paper/2303.06342/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (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)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution](https://mlsanity.com/paper/2105.05003/) (2021)
- [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022)
- [Probabilistic Oriented Object Detection in Automotive Radar](https://mlsanity.com/paper/2004.05310/) (2020)
- [DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather](https://mlsanity.com/paper/2109.07078/) (2021)
- [K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways](https://mlsanity.com/paper/2110.11048/) (2021)

## Built on by (most cited first, 15 of 15)

- [RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar](https://mlsanity.com/paper/2405.14014/) (2024)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding](https://mlsanity.com/paper/2310.17659/) (2023)
- [Efficient 4D Radar Data Auto-labeling Method using LiDAR-based Object Detection Network](https://mlsanity.com/paper/2407.04709/) (2024)
- [DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection](https://mlsanity.com/paper/2508.12330/) (2025)
- [Availability-aware Sensor Fusion via Unified Canonical Space](https://mlsanity.com/paper/2503.07029/) (2025)
- [4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges](https://mlsanity.com/paper/2503.24091/) (2025)
- [SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection](https://mlsanity.com/paper/2502.00074/) (2025)
- [RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather](https://mlsanity.com/paper/2602.19994/) (2026)
- [Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar](https://mlsanity.com/paper/2502.01357/) (2025)
- [4DR P2T: 4D Radar Tensor Synthesis with Point Clouds](https://mlsanity.com/paper/2502.05550/) (2025)
- [Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor](https://mlsanity.com/paper/2502.06114/) (2025)
- [Weather-Robust Scene Semantics with Vision-Aligned 4D Radar](https://mlsanity.com/paper/2605.07367/) (2026)
- [RadarXFormer: Robust Object Detection via Cross-Dimension Fusion of 4D Radar Spectra and Images for Autonomous Driving](https://mlsanity.com/paper/2603.14822/) (2026)
- [STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radar](https://mlsanity.com/paper/2609.24151/) (2026)

## Abstract (arXiv)

Recent works have shown the superior robustness of four-dimensional (4D) Radar-based three-dimensional (3D) object detection in adverse weather conditions. However, processing 4D Radar data remains a challenge due to the large data size, which require substantial amount of memory for computing and storage. In previous work, an online density reduction is performed on the 4D Radar Tensor (4DRT) to reduce the data size, in which the density reduction level is chosen arbitrarily. However, the impact of density reduction on the detection performance and memory consumption remains largely unknown. In this paper, we aim to address this issue by conducting extensive hyperparamter tuning on the density reduction level. Experimental results show that increasing the density level from 0.01% to 50% of the original 4DRT density level proportionally improves the detection performance, at a cost of memory consumption. However, when the density level is increased beyond 5%, only the memory consumption increases, while the detection performance oscillates below the peak point. In addition to the optimized density hyperparameter, we also introduce 4D Sparse Radar Tensor (4DSRT), a new representation for 4D Radar data with offline density reduction, leading to a significantly reduced raw data size. An optimized development kit for training the neural networks is also provided, which along with the utilization of 4DSRT, improves training speed by a factor of 17.1 compared to the state-of-the-art 4DRT-based neural networks. All codes are available at: https://github.com/kaist-avelab/K-Radar.