计算机科学
人工智能
体素
对象(语法)
计算机视觉
实体造型
深度学习
代表(政治)
构造(python库)
视觉对象识别的认知神经科学
卷积神经网络
二进制数
占用网格映射
计算机辅助设计
模式识别(心理学)
工程制图
数学
机器人
政治
法学
程序设计语言
工程类
政治学
算术
移动机器人
作者
Zhirong Wu,Shuran Song,Aditya Khosla,Fisher Yu,Linguang Zhang,Xiaoou Tang,Jianxiong Xiao
出处
期刊:Computer Vision and Pattern Recognition
日期:2015-06-01
被引量:1236
标识
DOI:10.1109/cvpr.2015.7298801
摘要
3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.
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