Large-Scale Shape Retrieval with Sparse 3D Convolutional Neural Networks

被引：9
|
作者：
Notchenko, Alexandr [1 ,2 ]
Kapushev, Yermek [1 ,2 ]
Burnaev, Evgeny [1 ]
机构：
[1] Skolkovo Innovat Ctr, Skolkovo Inst Sci & Technol, Bldg 3, Moscow 143026, Russia
[2] RAS, Inst Informat Transmiss Problems, Bolshoy Karetny Per 19,Build 1, Moscow 127051, Russia
来源：
ANALYSIS OF IMAGES, SOCIAL NETWORKS AND TEXTS, AIST 2017 | 2018年 / 10716卷
基金：
俄罗斯科学基金会;
关键词：
Deep Learning; Sparse 3D Convolutional Neural Network; Voxel resolution;
D O I：
10.1007/978-3-319-73013-4_23
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper we present results of performance evaluation of S3DCNN - a Sparse 3D Convolutional Neural Network - on a largescale 3D Shape benchmark ModelNet40, and measure how it is impacted by voxel resolution of input shape. We demonstrate comparable classification and retrieval performance to state-of-the-art models, but with much less computational costs in training and inference phases. We also notice that benefits of higher input resolution can be limited by an ability of a neural network to generalize high level features.
引用
收藏
页码：245 / 254
页数：10
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