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MCF3D: Multi-Stage Complementary Fusion for Multi-sensor 3D Object Detection
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DOI：10.1109/ACCESS.2019.2927012.png)
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摘要
En 中文
We present MCF3D, a multi-stage complementary fusion three-dimensional (3D) object detection network for autonomous driving, robot navigation, and virtual reality. This is an end-to-end learnable architecture, which takes both LIDAR point clouds and RGB images as inputs and utilizes a 3D region proposal subnet and second stage detector(s) subnet to achieve high-precision oriented 3D bounding box prediction. To fully exploit the strength of multimodal information, we design a series of fine and targeted fusion methods based on the attention mechanism and prior knowledge, including pre-fusion, anchorfusion, and proposal-fusion. Our proposed RGB-Intensity form encodes the reflection intensity onto the input image to strengthen the representational power. Our designed proposal-element attention module allows the network to be guided to focus more on efficient and critical information with negligible overheads. In addition, we propose a cascade-enhanced detector for small classes, which is more selective against close false positives. The experiments on the challenging KITTI benchmark show that our MCF3D method produces state-of-the-art results while running in near real-time with a low memory footprint.
Keyword:
3D object detection
multi-sensor fusion
attention mechanism
autonomous driving
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3.6
论文数:
9.8W
被引数:
29.4W
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