3D Pose Regression using Convolutional Neural Networks

被引：80

作者：

Mahendran, Siddharth ^{[1
]}

Ali, Haider ^{[1
]}

Vidal, Rene ^{[1
]}

机构：

[1] Johns Hopkins Univ, Ctr Imaging Sci, Baltimore, MD 21218 USA

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017) | 2017年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/ICCVW.2017.254

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin. We argue that the 3D pose space is continuous and propose to solve the pose estimation problem in a CNN regression framework with a suitable representation, data augmentation and loss function that captures the geometry of the pose space. Experiments on PASCAL3D+ show that the proposed 3D pose regression approach achieves competitive performance compared to the state-of-the-art.

引用

页码：2174 / 2182

页数：9