Search Results for author:
Found 14 papers, 6 papers with code
Osmosis: RGBD Diffusion Prior for Underwater Image Restoration
Using this prior together with a novel guidance method based on the underwater image formation model, we generate posterior samples of clean images, removing the water effects.
SeaThru-NeRF: Neural Radiance Fields in Scattering Media
Even more excitingly, we can render clear views of these scenes, removing the medium between the camera and the scene and reconstructing the appearance and depth of far objects, which are severely occluded by the medium.
Spatial Functa: Scaling Functa to ImageNet Classification and Generation
Neural fields, also known as implicit neural representations, have emerged as a powerful means to represent complex signals of various modalities.
From data to functa: Your data point is a function and you can treat it like one
A powerful continuous alternative is then to represent these measurements using an implicit neural representation, a neural function trained to output the appropriate measurement value for any input spatial location.
Unsupervised Doodling and Painting with Improved SPIRAL
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804. 01118).
Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions.
Learning models for visual 3D localization with implicit mapping
We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels.
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
Neural Processes
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.
Beyond Brightness Constancy: Learning Noise Models for Optical Flow
Consistent with current practice, we find that robust versions of gradient constancy are better models than simple brightness constancy but a learned GMM that models the density of patches of warp error gives a much better fit than any existing assumption of constancy.
Statistics of RGBD Images
We then use the generative models together with a degradation model and obtain a Bayes Least Squares (BLS) estimator of the D channel given the RGB channels.
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors
In this paper we show how to combine the strengths of both approaches by training a discriminative, feed-forward architecture to predict the state of latent variables in a generative model of natural images.
Subspace Learning with Partial Information
The goal of subspace learning is to find a $k$-dimensional subspace of $\mathbb{R}^d$, such that the expected squared distance between instance vectors and the subspace is as small as possible.
Learning the Local Statistics of Optical Flow
Motivated by recent progress in natural image statistics, we use newly available datasets with ground truth optical flow to learn the local statistics of optical flow and rigorously compare the learned model to prior models assumed by computer vision optical flow algorithms.
