  3D vehicle detection based on multi-modal fusion is an important task of many
applications such as autonomous driving. Although significant progress has been
made, we still observe two aspects that need to be further improvement: First,
the specific gain that camera images can bring to 3D detection is seldom
explored by previous works. Second, many fusion algorithms run slowly, which is
essential for applications with high real-time requirements(autonomous
driving). To this end, we propose an end-to-end trainable single-stage
multi-modal feature adaptive network in this paper, which uses image
information to effectively reduce false positive of 3D detection and has a fast
detection speed. A multi-modal adaptive feature fusion module based on channel
attention mechanism is proposed to enable the network to adaptively use the
feature of each modal. Based on the above mechanism, two fusion technologies
are proposed to adapt to different usage scenarios: PointAttentionFusion is
suitable for filtering simple false positive and faster; DenseAttentionFusion
is suitable for filtering more difficult false positive and has better overall
performance. Experimental results on the KITTI dataset demonstrate significant
improvement in filtering false positive over the approach using only point
cloud data. Furthermore, the proposed method can provide competitive results
and has the fastest speed compared to the published state-of-the-art
multi-modal methods in the KITTI benchmark.
