[talks] Yinda Zhang will present his FPO "From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments" on Thursday, 10/25/2018 at 3:00 pm in CS 302.

[Nicki Gotsis]

Yinda Zhang will present his FPO "From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments" on Thursday, 10/25/2018 at 3:00 pm in CS 302. 




The members of his committee are as follows: Thomas Funkhouser (adviser); Examiners: Ryan Adams, Olga Russakovsky, and Thomas Funkhouser; Readers: Szymon Rusinkiewicz and James Hays (Georgia Institute of Technology) 




A copy of his thesis is available upon request. 




Everyone is invited to attend her talk. The talk title and abstract follow below: 


Understanding the 3D geometry and semantics of real environments is in critically 
high demand for many applications, such as autonomous driving, robotics, and augmented 
reality. However, it is extremely challenging due to imperfect and noisy measurements 
from real sensors, limited access to ground truth data, and cluttered scenes 
exhibiting heavy occlusions and intervening objects. To address these issues, this thesis 
introduces a series of works that produce a geometric and semantic understanding 
of the scene in both pixel-wise and holistic 3D representations. Starting from estimating 
a depth map, which is a fundamental task in many approaches for reconstructing 
the 3D geometry of the scene, we introduce a learning-based active stereo system 
that is trained in a self-supervised fashion and reduces the disparity error to 1/10th 
of other canonical stereo systems. To handle a more common case where only one 
input image is available for scene understanding, we create a high-quality synthetic 
dataset facilitating pre-training of data-driven approaches, and demonstrating that 
we can improve the surface normal estimation and improve raw depth measurements 
from commodity RGBD sensors. Lastly, we pursue holistic 3D scene understanding 
by estimating a 3D representation of the scene, in which objects and room layout 
are represented using 3D bounding box and planar surfaces respectively. We propose 
methods to produce such a representation from either a single color panorama or a 
depth image, leveraging scene context. On the whole, these proposed methods produce 
understanding of both 3D geometry and semantics from the most ne-grained 
pixel level to the holistic scene scale, building foundations that support future work 
in 3D scene understanding. 
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