  Multi-modal 3D object detection has exhibited significant progress in recent
years. However, most existing methods can hardly scale to long-range scenarios
due to their reliance on dense 3D features, which substantially escalate
computational demands and memory usage. In this paper, we introduce
SparseFusion, a novel multi-modal fusion framework fully built upon sparse 3D
features to facilitate efficient long-range perception. The core of our method
is the Sparse View Transformer module, which selectively lifts regions of
interest in 2D image space into the unified 3D space. The proposed module
introduces sparsity from both semantic and geometric aspects which only fill
grids that foreground objects potentially reside in. Comprehensive experiments
have verified the efficiency and effectiveness of our framework in long-range
3D perception. Remarkably, on the long-range Argoverse2 dataset, SparseFusion
reduces memory footprint and accelerates the inference by about two times
compared to dense detectors. It also achieves state-of-the-art performance with
mAP of 41.2% and CDS of 32.1%. The versatility of SparseFusion is also
validated in the temporal object detection task and 3D lane detection task.
Codes will be released upon acceptance.
