MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data

• URL: http://arxiv.org/abs/2104.10772v1
• Date: Wed, 21 Apr 2021 21:29:08 GMT
• Title: MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data
• Authors: Ankit Laddha, Shivam Gautam, Stefan Palombo, Shreyash Pandey, Carlos Vallespi-Gonzalez
• Abstract summary: We propose itMVFusenet, a novel end-to-end method for joint object detection motion forecasting from a temporal sequence of LiDAR data. We show the benefits of our multi-view approach for the tasks of detection and motion forecasting on two large-scale self-driving data sets.
• Score: 4.8061970432391785
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we propose \textit{MVFuseNet}, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data. Most existing methods operate in a single view by projecting data in either range view (RV) or bird's eye view (BEV). In contrast, we propose a method that effectively utilizes both RV and BEV for spatio-temporal feature learning as part of a temporal fusion network as well as for multi-scale feature learning in the backbone network. Further, we propose a novel sequential fusion approach that effectively utilizes multiple views in the temporal fusion network. We show the benefits of our multi-view approach for the tasks of detection and motion forecasting on two large-scale self-driving data sets, achieving state-of-the-art results. Furthermore, we show that MVFusenet scales well to large operating ranges while maintaining real-time performance.

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