  Scene understanding based on LiDAR point cloud is an essential task for
autonomous cars to drive safely, which often employs spherical projection to
map 3D point cloud into multi-channel 2D images for semantic segmentation. Most
existing methods simply stack different point attributes/modalities (e.g.
coordinates, intensity, depth, etc.) as image channels to increase information
capacity, but ignore distinct characteristics of point attributes in different
image channels. We design FPS-Net, a convolutional fusion network that exploits
the uniqueness and discrepancy among the projected image channels for optimal
point cloud segmentation. FPS-Net adopts an encoder-decoder structure. Instead
of simply stacking multiple channel images as a single input, we group them
into different modalities to first learn modality-specific features separately
and then map the learned features into a common high-dimensional feature space
for pixel-level fusion and learning. Specifically, we design a residual dense
block with multiple receptive fields as a building block in the encoder which
preserves detailed information in each modality and learns hierarchical
modality-specific and fused features effectively. In the FPS-Net decoder, we
use a recurrent convolution block likewise to hierarchically decode fused
features into output space for pixel-level classification. Extensive
experiments conducted on two widely adopted point cloud datasets show that
FPS-Net achieves superior semantic segmentation as compared with
state-of-the-art projection-based methods. In addition, the proposed modality
fusion idea is compatible with typical projection-based methods and can be
incorporated into them with consistent performance improvements.
