Search Results for author:
Found 11 papers, 6 papers with code
Burst Denoising via Temporally Shifted Wavelet Transforms
As a result, imaging pipelines often rely on computational photography to improve SNR by fusing multiple short exposures.
Perspective Fields for Single Image Camera Calibration
We propose perspective fields as a representation that models the local perspective properties of an image.
One Shot 3D Photography
3D photos are static in time, like traditional photos, but are displayed with interactive parallax on mobile or desktop screens, as well as on Virtual Reality devices, where viewing it also includes stereo.
Consistent Video Depth Estimation
We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video.
GeoStyle: Discovering Fashion Trends and Events
Understanding fashion styles and trends is of great potential interest to retailers and consumers alike.
Synthetic Defocus and Look-Ahead Autofocus for Casual Videography
We present a system that synthetically renders refocusable video from a deep DOF video shot with a smartphone, and analyzes future video frames to deliver context-aware autofocus for the current frame.
DeepMVS: Learning Multi-view Stereopsis
We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction.
Deep Burst Denoising
One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance.
StreetStyle: Exploring world-wide clothing styles from millions of photos
Each day billions of photographs are uploaded to photo-sharing services and social media platforms.
All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles
A novel probabilistic perception algorithm is presented as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle in all-weather conditions.
BubbLeNet: Foveated Imaging for Visual Discovery
We propose a new method for turning an Internet-scale corpus of categorized images into a small set of human-interpretable discriminative visual elements using powerful tools based on deep learning.
