MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding

• URL: http://arxiv.org/abs/2107.00346v1
• Date: Thu, 1 Jul 2021 10:19:32 GMT
• Title: MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
• Authors: Kunyu Peng, Juncong Fei, Kailun Yang, Alina Roitberg, Jiaming Zhang, Frank Bieder, Philipp Heidenreich, Christoph Stiller, Rainer Stiefelhagen
• Abstract summary: We introduce MASS - a Multi-Attentional Semantic model for dense top-view understanding of driving scenes. Our framework operates on pillar- and occupancy features and comprises three attention-based building blocks. Our model is shown to be very effective for 3D object detection validated on the KITTI-3D dataset.
• Score: 27.867824780748606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At the heart of all automated driving systems is the ability to sense the surroundings, e.g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progress due to the release of large datasets such as SemanticKITTI and nuScenes-LidarSeg. While most previous works focus on sparse segmentation of the LiDAR input, dense output masks provide self-driving cars with almost complete environment information. In this paper, we introduce MASS - a Multi-Attentional Semantic Segmentation model specifically built for dense top-view understanding of the driving scenes. Our framework operates on pillar- and occupancy features and comprises three attention-based building blocks: (1) a keypoint-driven graph attention, (2) an LSTM-based attention computed from a vector embedding of the spatial input, and (3) a pillar-based attention, resulting in a dense 360-degree segmentation mask. With extensive experiments on both, SemanticKITTI and nuScenes-LidarSeg, we quantitatively demonstrate the effectiveness of our model, outperforming the state of the art by 19.0% on SemanticKITTI and reaching 32.7% in mIoU on nuScenes-LidarSeg, where MASS is the first work addressing the dense segmentation task. Furthermore, our multi-attention model is shown to be very effective for 3D object detection validated on the KITTI-3D dataset, showcasing its high generalizability to other tasks related to 3D vision.

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