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Found 13 papers, 1 papers with code
RoGUENeRF: A Robust Geometry-Consistent Universal Enhancer for NeRF
Conversely, existing 3D enhancers are able to transfer detail from nearby training images in a generalizable manner, but suffer from inaccurate camera calibration and can propagate errors from the geometry into rendered images.
SWAGS: Sampling Windows Adaptively for Dynamic 3D Gaussian Splatting
We model the dynamics of a scene using a tunable MLP, which learns the deformation field from a canonical space to a set of 3D Gaussians per frame.
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields.
Human Gaussian Splatting: Real-time Rendering of Animatable Avatars
This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
HDR Reconstruction from Bracketed Exposures and Events
Our proposed approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the HdM HDR dataset, respectively.
Acquisition-invariant brain MRI segmentation with informative uncertainties
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses.
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases.
A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation
We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality.
Estimating MRI Image Quality via Image Reconstruction Uncertainty
Thus, we argue that quality control for visual assessment cannot be equated to quality control for algorithmic processing.
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions.
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner.
Question Answering Against Very-Large Text Collections
Question answering involves developing methods to extract useful information from large collections of documents.
