201 research outputs found
Deep Mean-Shift Priors for Image Restoration
In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our prior corresponds to the mean-shift vector on the natural image
distribution. In addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes risk
minimization. We demonstrate competitive results for noise-blind deblurring,
super-resolution, and demosaicing.Comment: NIPS 201
Challenges in Disentangling Independent Factors of Variation
We study the problem of building models that disentangle independent factors
of variation. Such models could be used to encode features that can efficiently
be used for classification and to transfer attributes between different images
in image synthesis. As data we use a weakly labeled training set. Our weak
labels indicate what single factor has changed between two data samples,
although the relative value of the change is unknown. This labeling is of
particular interest as it may be readily available without annotation costs. To
make use of weak labels we introduce an autoencoder model and train it through
constraints on image pairs and triplets. We formally prove that without
additional knowledge there is no guarantee that two images with the same factor
of variation will be mapped to the same feature. We call this issue the
reference ambiguity. Moreover, we show the role of the feature dimensionality
and adversarial training. We demonstrate experimentally that the proposed model
can successfully transfer attributes on several datasets, but show also cases
when the reference ambiguity occurs.Comment: Submitted to ICLR 201
Disentangling Factors of Variation by Mixing Them
We propose an approach to learn image representations that consist of
disentangled factors of variation without exploiting any manual labeling or
data domain knowledge. A factor of variation corresponds to an image attribute
that can be discerned consistently across a set of images, such as the pose or
color of objects. Our disentangled representation consists of a concatenation
of feature chunks, each chunk representing a factor of variation. It supports
applications such as transferring attributes from one image to another, by
simply mixing and unmixing feature chunks, and classification or retrieval
based on one or several attributes, by considering a user-specified subset of
feature chunks. We learn our representation without any labeling or knowledge
of the data domain, using an autoencoder architecture with two novel training
objectives: first, we propose an invariance objective to encourage that
encoding of each attribute, and decoding of each chunk, are invariant to
changes in other attributes and chunks, respectively; second, we include a
classification objective, which ensures that each chunk corresponds to a
consistently discernible attribute in the represented image, hence avoiding
degenerate feature mappings where some chunks are completely ignored. We
demonstrate the effectiveness of our approach on the MNIST, Sprites, and CelebA
datasets.Comment: CVPR 201
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Object Space EWA Surface Splatting: A Hardware Accelerated Approach to High Quality Point Rendering
Elliptical weighted average (EWA) surface splatting is a technique for high quality rendering of point-sampled 3D objects. EWA surface splatting renders water-tight surfaces of complex point models with high quality, anisotropic texture filtering. In this paper we introduce a new multi-pass approach to perform EWA surface splatting on modern PC graphics hardware, called object space EWA splatting. We derive an object space formulation of the EWA filter, which is amenable for acceleration by conventional triangle-based graphics hardware. We describe how to implement the object space EWA filter using a two pass rendering algorithm. In the first rendering pass, visibility splatting is performed by shifting opaque surfel polygons backward along the viewing rays, while in the second rendering pass view-dependent EWA prefiltering is performed by deforming texture mapped surfel polygons. We use texture mapping and alpha blending to facilitate the splatting process. We implement our algorithm using programmable vertex and pixel shaders, fully exploiting the capabilities of today’s graphics processing units (GPUs). Our implementation renders up to 3 million points per second on recent PC graphics hardware, an order of magnitude more than a pure software implementation of screen space EWA surface splatting.Engineering and Applied Science
3D Shape Completion with Multi-view Consistent Inference
3D shape completion is important to enable machines to perceive the complete
geometry of objects from partial observations. To address this problem,
view-based methods have been presented. These methods represent shapes as
multiple depth images, which can be back-projected to yield corresponding 3D
point clouds, and they perform shape completion by learning to complete each
depth image using neural networks. While view-based methods lead to
state-of-the-art results, they currently do not enforce geometric consistency
among the completed views during the inference stage. To resolve this issue, we
propose a multi-view consistent inference technique for 3D shape completion,
which we express as an energy minimization problem including a data term and a
regularization term. We formulate the regularization term as a consistency loss
that encourages geometric consistency among multiple views, while the data term
guarantees that the optimized views do not drift away too much from a learned
shape descriptor. Experimental results demonstrate that our method completes
shapes more accurately than previous techniques.Comment: Accepted to AAAI 2020 as oral presentatio
Anisotropic noise
Programmable graphics hardware makes it possible to generate procedural noise textures on the fly for interactive rendering. However, filtering and antialiasing procedural noise involves a tradeoff between aliasing artifacts and loss of detail. In this paper we present a technique, targeted at interactive applications, that provides high-quality anisotropic filtering for noise textures. We generate noise tiles directly in the frequency domain by partitioning the frequency domain into oriented subbands. We then compute weighted sums of the subband textures to accurately approximate noise with a desired spectrum. This allows us to achieve high-quality anisotropic filtering. Our approach is based solely on 2D textures, avoiding the memory overhead of techniques based on 3D noise tiles. We devise a technique to compensate for texture distortions to generate uniform noise on arbitrary meshes. We develop a GPU-based implementation of our technique that achieves similar rendering performance as state-of-the-art algorithms for procedural noise. In addition, it provides anisotropic filtering and achieves superior image quality.National Science Foundation (U.S.) (CAREER Award 0447561)Microsoft Research (New Faculty Fellowship)Alfred P. Sloan Foundation (Fellowship
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