Depth Estimation with OpenCV Python for 3D Object Detection

YOUR LINK HERE:

http://youtube.com/watch?v=8CenT_4HWyY

Leonidas Guibas; Michael Bronstein; Evangelos Kalogerakis; Qixing Huang; Jimei Yang;Hao Su;Charles Qi • Understanding 3D data has been attracting • increasing attention recently due to its importance for • many vision systems, such as self-driving cars, • autonomous robots, augmented reality and medical • image processing. This tutorial covers deep learning • algorithms for 3D geometric data. Different from 2D • images that have a dominant representation as arrays, • 3D geometric data have multiple popular representations, • ranging from point cloud, meshes, volumetric field to • multi-view images, each fitting their own application • scenarios. Each type of data format has its own • properties that pose challenges to deep architecture • design while also provides the opportunity for novel and • efficient solutions. In this course, we will introduce the • major advance of deep learning for each of the 3D • representation types. We systematically introduce topics • such as the characteristics of each representation type, • how to encode them as neural network input and output, • and what are the keys in the design of corresponding • network structures. Through the course, the audience will • learn the big picture of cutting-edge techniques as well • as open problems in the field. For schedule and course • material, please check http://3ddl.stanford.edu.

#############################

New on site