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Filtering by: Creator Zwieniecki, Maciej A. Advisor Li, Fuxin

Deep Perception Without a Camera: Enabling 3D Reconstruction and Object Recognition using Lidar and Sonar Sensing

Creator:

DeBortoli, Robert A.

Abstract:

Deep learning has recently revolutionized robot perception in many canonical robotic applications, such as autonomous driving. However, a similar transformation has yet to occur in more harsh environments including underwater and underground. This is due in part to the difficulty in deploying robots in these environments, which lack large real...

Resource Type:

Dissertation

Full Text:

Without a Camera: Enabling 3D Reconstruction and Object Recognition using Lidar and Sonar Sensing
Leveraging Structures of the Data in Deep Learning

Creator:

Li, Xingyi

Abstract:

The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...

Resource Type:

Dissertation

Full Text:

justify that, for convolutional neural networks (CNNs), neighborhoods of a pixel should be redefined as
Deep Convolutional Network on Point Clouds for 3D Scene Understanding

Creator:

Wu, Wenxuan

Abstract:

As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...

Resource Type:

Dissertation

Full Text:

in those applications as depth information could remove a lot of the segmentation ambiguities in 2D