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Found 13 papers, 9 papers with code
Progressively Optimized Local Radiance Fields for Robust View Synthesis
For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner.
DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs
Re-rendering the portrait at a proper focal length and camera distance effectively corrects perspective distortions and produces more natural-looking results.
Robust Dynamic Radiance Fields
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision
In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack).
Hybrid Neural Fusion for Full-frame Video Stabilization
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
Explorable Tone Mapping Operators
It aims to preserve visual information of HDR images in a medium with a limited dynamic range.
Learning Camera-Aware Noise Models
Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications.
Learning to See Through Obstructions with Layered Decomposition
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.
Learning to See Through Obstructions
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
Attention-based View Selection Networks for Light-field Disparity Estimation
This paper introduces a novel deep network for estimating depth maps from a light field image.
Ranked #1 on
Depth Estimation
on 4D Light Field Dataset
Deep Video Frame Interpolation using Cyclic Frame Generation
In addition to the cycle consistency loss, we propose two extensions: motion linearity loss and edge-guided training.
