The KITTI Vision Benchmark Suite

In this work, we propose a novel semantic passing
framework, named SPNet, to boost the performance
of existing lidar-based 3D detection models with
the guidance of rich context painting, with no
extra computation cost during inference. Our key
design is to first exploit the potential
instructive semantic knowledge within the ground-
truth labels by training a semantic-painted
teacher model and then guide the pure-lidar
network to learn the semantic-painted
representation via knowledge passing modules at
different granularities: class-wise passing,
pixel-wise passing and instance-wise passing.