# DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather

Source: [DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather](<https://www.sota2.com/research/paper/69d8842b7d0172140ec77adf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-11T05:00:04.811Z

Authors: Christof Leitgeb, Thomas Puchleitner, Max Peter Ronecker, Daniel Watzenig

Published: 2026-04-09T10:46:41.000Z

## Abstract

Reliable and weather-robust perception systems are essential for safe autonomous driving and typically employ multi-modal sensor configurations to achieve comprehensive environmental awareness. While recent automotive FMCW Radar-based approaches achieved remarkable performance on detection tasks in adverse weather conditions, they exhibited limitations in resolving fine-grained spatial details particularly critical for detecting smaller and vulnerable road users (VRUs). Furthermore, existing research has not adequately addressed VRU detection in adverse weather datasets such as K-Radar. We present DinoRADE, a Radar-centered detection pipeline that processes dense Radar tensors and aggregates vision features around transformed reference points in the camera perspective via deformable cross-attention. Vision features are provided by a DINOv3 Vision Foundation Model. We present a comprehensive performance evaluation on the K-Radar dataset in all weather conditions and are among the first to report detection performance individually for five object classes. Additionally, we compare our method with existing single-class detection approaches and outperform recent Radar-camera approaches by 12.1%. The code is available under https://github.com/chr-is-tof/RADE-Net.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [3D Object Detection on K-Radar Sedan v1.1 (test)](<https://www.sota2.com/research/sota/3d-object-detection-on-k-radar-sedan-v1-1-test.md>): 3D Object Detection; K-Radar Sedan v1.1 (test); Total AP: 70.8.

- [3D Object Detection on K-Radar v2.1](<https://www.sota2.com/research/sota/3d-object-detection-on-k-radar-v2-1.md>): 3D Object Detection; K-Radar v2.1; Mean AP Total (3D): 36.99.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.08074>)

[PDF](<https://arxiv.org/pdf/2604.08074.pdf>)