  In this paper, we present an Assertion-based Multi-View Fusion network
(AMVNet) for LiDAR semantic segmentation which aggregates the semantic features
of individual projection-based networks using late fusion. Given class scores
from different projection-based networks, we perform assertion-guided point
sampling on score disagreements and pass a set of point-level features for each
sampled point to a simple point head which refines the predictions. This
modular-and-hierarchical late fusion approach provides the flexibility of
having two independent networks with a minor overhead from a light-weight
network. Such approaches are desirable for robotic systems, e.g. autonomous
vehicles, for which the computational and memory resources are often limited.
Extensive experiments show that AMVNet achieves state-of-the-art results in
both the SemanticKITTI and nuScenes benchmark datasets and that our approach
outperforms the baseline method of combining the class scores of the
projection-based networks.
