Loading...

Saurabh Gupta: Scene Understanding from RGB-D Images

1,789 views

Loading...

Loading...

Loading...

Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on May 15, 2016

Saurabh Gupta: Scene Understanding from RGB-D Images

Abstract:

In this talk, I will talk about detailed scene understanding from RGB-D images. We approach this problem by studying central computer vision problems like bottom-up grouping, object detection, instance segmentation, pose estimation in context of RGB-D images, and finally aligning CAD models to objects in the scene. This results in a detailed output which goes beyond what most current computer vision algorithms produce, and is useful for real world applications like perceptual robotics, and augmented reality. A central question in this work is how to learn good features for depth images in view of the fact that labeled RGB-D datasets are much smaller than labeled RGB datasets (such as ImageNet) typically used for feature learning. To this end I will describe our technique called "cross-modal distillation" which allows us to leverage easily available annotations on RGB images to learn representations on depth images. In addition, I will also briefly talk about some work on vision and language that I did on an internship at Microsoft Research.

Loading...

When autoplay is enabled, a suggested video will automatically play next.

Up next


to add this to Watch Later

Add to

Loading playlists...