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

页码：245 / 254

页数：10

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