Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting
  through Multi-View Fusion of LiDAR Data   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.

3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg