The KITTI Vision Benchmark Suite

Camera and lidar are considered as important
sensor to achieve higher-level autonomous driving.
And the complementary information provided by
these sensors offer more opportunities for
improving performance. However, it is difficulty
to fuse them because of different representation
of both. In this work, we propose a novel fusion
framework SGFusion, which fuse the image and point
clouds at semantic and geometric level. The
SGFusion framework is divided in sequence into two
stages: semantic fusion stage and geometric fusion
stage. First, the point clouds are painted with
the object-level semantic information obtained
from an 2D object detector on the semantic fusion
stage . At the same time, the output candidates
of 2D object detector will be saved. Then, the
painted point clouds fed to one LiDAR-based
detector for obtaining high-quality 3D detection
candidates. Finally, on the geometric fusion
stage, these 3D detection candidates and the 2D
detection candidates just saved are combin

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