Search Results for author:
Found 10 papers, 3 papers with code
Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging
Traditional cameras face a trade-off between low-light performance and high-speed imaging: longer exposure times to capture sufficient light results in motion blur, whereas shorter exposures result in Poisson-corrupted noisy images.
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images
It learns photorealistic scene representations by decomposing users' 3D views into personal and global NeRFs and a novel optimally weighted aggregation of only the latter.
SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition
3D modeling from satellite imagery is essential in areas of environmental science, urban planning, agriculture, and disaster response.
Role of Transients in Two-Bounce Non-Line-of-Sight Imaging
Specifically, we study how ToF information can reduce the number of measurements and spatial resolution needed for shape reconstruction.
ORCa: Glossy Objects As Radiance-Field Cameras
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
ORCa: Glossy Objects as Radiance Field Cameras
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
PANDORA: Polarization-Aided Neural Decomposition Of Radiance
Reconstructing an object's geometry and appearance from multiple images, also known as inverse rendering, is a fundamental problem in computer graphics and vision.
Thermal Image Processing via Physics-Inspired Deep Networks
We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation.
Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior
On the other hand, a more flexible approach would be to learn a deep generative model once and then use it as a signal prior for solving various inverse problems.
Compressive Image Recovery Using Recurrent Generative Model
Reconstruction of signals from compressively sensed measurements is an ill-posed problem.
