Author
Balatkan, Eren, Kıraç, Mustafa Furkan
Publication Date
2021
Publication Place
-
IEEE
Subject
3D object detection, Bin mixing, Sparse voxel grid, Sub-manifold sparse convolution
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2-s2.0-85125842056
Record ID
e5410971-9a53-4eca-9546-3bb861a86210
Library Location
Computer Science
Date
2021
Sample Text
Accurate and fast 3D object detection plays a role of paramount importance for safe and capable autonomous machines. LiDAR point cloud based methods have demonstrated impressive results, yet expensive LiDAR sensors make such approaches infeasible for wide-scale adaptation. Camera based methods on the other hand are performing sub-optimally given safety and accuracy requirements. Traditionally, camera based 3D object detection is performed by generating pseudo-LiDAR point clouds from RGB-D data and using point-cloud based models, however, irregular nature of point cloud data representation makes it challenging to exploit spatial local correlations on 3D space and point cloud based models generally suffer from this. To this end, we propose Sparse Voxel based 3D Object Detection, our approach differs from traditional approaches by converting point cloud information to sparse voxel grid and utilizing sub-manifold sparse convolutions to extract information instead of PointNet based models. Furthermore, we propose Bin-Mixing layers. Bin-Mixing replaces the output layer of a neural network and boosts performance by representing the problem of regression in a fashion that is easier for network to learn.
DOI
10.1109/UBMK52708.2021.9558880
