FarDetect3D: Enhancing Long-Range Object Detection in Multi-View 3D Systems

In this paper, we propose FarDetect3D, a novel multi-view 3D detection paradigm, to enhance the detection of long-range objects. FarDetect3D improves the existing sparse query-based multi-view 3D object detection by introducing two modules: Remote Detection Denoising ReDN and Long-range Feature Attention LrFA . ReDN utilizes the fake long-range depth information to generate sparse 3D queries, which improves the performance of the long-range detection. LrFA enhances the central features and captures the contextual relationships between the distant pixels, further boosting the detection accuracy. Experimental results show that our approach outperforms the state-of-the-art camera-based multi-view 3D detection methods, which can provide a robust solution for safe autonomous driving in complex environments.