Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View
  General-Purpose 3D Object Detection   In this paper, we introduce the task of multi-view RGB-based 3D object
detection as an end-to-end optimization problem. To address this problem, we
propose ImVoxelNet, a novel fully convolutional method of 3D object detection
based on monocular or multi-view RGB images. The number of monocular images in
each multi-view input can variate during training and inference; actually, this
number might be unique for each multi-view input. ImVoxelNet successfully
handles both indoor and outdoor scenes, which makes it general-purpose.
Specifically, it achieves state-of-the-art results in car detection on KITTI
(monocular) and nuScenes (multi-view) benchmarks among all methods that accept
RGB images. Moreover, it surpasses existing RGB-based 3D object detection
methods on the SUN RGB-D dataset. On ScanNet, ImVoxelNet sets a new benchmark
for multi-view 3D object detection. The source code and the trained models are
available at https://github.com/saic-vul/imvoxelnet.

20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio