AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2012.04934v1
• Date: Wed, 9 Dec 2020 09:34:25 GMT
• Title: AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation
• Authors: Venice Erin Liong, Thi Ngoc Tho Nguyen, Sergi Widjaja, Dhananjai Sharma, Zhuang Jie Chong
• Abstract summary: We present an Assertion-based Multi-View Fusion network (AMVNet) for LiDAR semantic segmentation. 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. Our approach outperforms the baseline method of combining the class scores of the projection-based networks.
• Score: 8.883837682023493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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