AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation
arXiv:2012.04934
Abstract
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.
10 pages, 7 figures, 5 tables
References in corpus (5)
- Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
- SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
- KPRNet: Improving projection-based LiDAR semantic segmentation
- Model Assertions for Monitoring and Improving ML Models
- TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module