# Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation Source: [Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:31:06.454Z Authors: Jianning Deng, Gabriel Chan, Hantao Zhong, Chris Xiaoxuan Lu Published: 2023-09-29T15:46:59.000Z ## Abstract This paper presents a novel framework for robust 3D object detection from point clouds via cross-modal hallucination. Our proposed approach is agnostic to either hallucination direction between LiDAR and 4D radar. We introduce multiple alignments on both spatial and feature levels to achieve simultaneous backbone refinement and hallucination generation. Specifically, spatial alignment is proposed to deal with the geometry discrepancy for better instance matching between LiDAR and radar. The feature alignment step further bridges the intrinsic attribute gap between the sensing modalities and stabilizes the training. The trained object detection models can deal with difficult detection cases better, even though only single-modal data is used as the input during the inference stage. Extensive experiments on the View-of-Delft (VoD) dataset show that our proposed method outperforms the state-of-the-art (SOTA) methods for both radar and LiDAR object detection while maintaining competitive efficiency in runtime. Code is available at https://github.com/DJNing/See\_beyond\_seeing. ## 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. ## Original sources and implementation [Original paper]() [PDF]()