Horizon3D: Sparse Radar-Camera Fusion for Long-Range 3D Perception in Autonomous Driving

Long-range 3D object detection is critical for safe autonomous driving at highway speeds, yet existing radar-camera fusion methods face notable limitations at extended ranges. BEV-based approaches effectively encode scene-level context but incur rapidly growing computational cost and struggle to preserve fine-grained object-level detail, while querybased methods provide efficient object-centric encoding but lack sufficient scene-level context. Temporal fusion introduces additional challenges: distant objects produce only a few radar returns and occupy only a few image pixels, requiring scene-level accumulation, while high-speed motion causes large inter-frame displacements that require object-level motion modeling. BEV-based aggregation alleviates sparsity through multi-frame accumulation but is less suited to individual object motion, whereas query-based modeling captures object-level motion but provides limited scene-level temporal context. In this paper, we propose Horizon3D, a sparse radar-camera fusion framework for long-range 3D object detection that jointly captures object-level detail and scene-level context in both spatial and temporal dimensions through a hybrid representation that combines Gaussian primitives with sparse BEV features. Horizon3D first employs Keypoint-Guided Gaussian Initialization (KGGI) to initialize Gaussian primitives at object keypoints estimated from radar and camera features. Object-Centric Sparse Fusion (OCSF) aggregates cross-modal features around these primitives and splats the refined Gaussians onto the BEV plane, where they are fused with sparse radar BEV features to combine object-level detail with scene-level context. Finally, Dual-Path Temporal Fusion (DPTF) aggregates temporal cues through a BEV path for multi-frame feature accumulation and a Gaussian path for propagating primitives across frames to encode per-object motion. Extensive evaluations on TruckScenes demonstrate that Horizon3D achieves stateof-the-art performance for radar-camera 3D object detection. On the validation set, our approach outperforms the previous best method by +3.0 NDS and +1.6 mAP while maintaining a sparse representation with competitive inference speed.