# SGE-Flow: 4D mmWave Radar 3D Object Detection via Spatiotemporal Geometric Enhancement and Inter-Frame Flow

## TL;DR

This paper introduces SGE-Flow, a new 4D radar object detection system that improves accuracy and speed using spatiotemporal enhancements and motion inference.

## Contribution

The novel contribution is the integration of lightweight spatiotemporal geometric enhancements and a Transformer-based motion inference module for 4D radar perception.

## Key findings

- SGE-Flow achieves 53.23% 3D mean Average Precision on the VoD dataset.
- The system runs at 72 FPS on an NVIDIA RTX 3090 while maintaining accuracy.
- The proposed modules are effective in improving other strong baselines like MAFF-Net.

## Abstract

4D millimeter-wave radar provides a promising solution for robust perception in adverse weather. Existing detectors still struggle with sparse and noisy point clouds, and maintaining real-time inference while achieving competitive accuracy remains challenging. We propose SGE-Flow, a streamlined PointPillars-based 4D radar 3D detector that embeds lightweight spatiotemporal geometric enhancements into the voxelization front-end. Velocity Displacement Compensation (VDC) leverages compensated radial velocity to align accumulated points in physical space and improve geometric consistency. Distribution-Aware Density (DAD) enables fast density feature extraction by estimating per-pillar density from simple statistical moments, which also restores vertical distribution cues lost during pillarization. To compensate for the absence of tangential velocity measurements, a Transformer-based Inter-frame Flow (IFF) module infers latent motion from frame-to-frame pillar occupancy changes. Evaluations on the View-of-Delft (VoD) dataset show that SGE-Flow achieves 53.23% 3D mean Average Precision (mAP) while running at 72 frames per second (FPS) on an NVIDIA RTX 3090. The proposed modules are plug-and-play and can also improve strong baselines such as MAFF-Net.