Publications

End-to-end Learning of Driving Models from Large-scale Video Datasets

End-to-end Learning of Driving Models from Large-scale Video Datasets

CVPR 2017 Oral We develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state.

Scribbler: Controlling Deep Image Synthesis with Sketch and Color

Scribbler: Controlling Deep Image Synthesis with Sketch and Color

CVPR 2017 We propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic images.

Semantic Scene Completion from a Single Depth Image

Semantic Scene Completion from a Single Depth Image

CVPR 2017 Oral Our network uses a dilation-based 3D context module to efficiently expand the receptive field and enable 3D context learning.

SHREC’17 Track Large-Scale 3D Shape Retrieval from ShapeNet Core55

SHREC’17 Track Large-Scale 3D Shape Retrieval from ShapeNet Core55

3DOR 2017 This track provides a benchmark to evaluate large-scale 3D shape retrieval based on the ShapeNet dataset.

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

arXiv 2016 We introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.

Automatic Triage for a Photo Series

Automatic Triage for a Photo Series

Siggraph 2016 We seek a relative quality measure within a series of photos taken of the same scene, which can be used for automatic photo triage.

Multi-Scale Context Aggregation by Dilated Convolutions

Multi-Scale Context Aggregation by Dilated Convolutions

ICLR 2016 We study dilated convolution in depth. It has become a foundamental network operation.

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

arXiv 2015 We propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop.

Semantic Alignment of LiDAR Data at City Scale

Semantic Alignment of LiDAR Data at City Scale

CVPR 2015 We propose an automatic algorithm for global alignment of LiDAR data collected with Google Street View cars in urban environments.