Search Results for author: Richard Shaw

Found 13 papers, 1 papers with code

RoGUENeRF: A Robust Geometry-Consistent Universal Enhancer for NeRF

no code implementations18 Mar 2024 Sibi Catley-Chandar, Richard Shaw, Gregory Slabaugh, Eduardo Perez-Pellitero

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.

3D Scene Reconstruction Camera Calibration +2

SWAGS: Sampling Windows Adaptively for Dynamic 3D Gaussian Splatting

no code implementations20 Dec 2023 Richard Shaw, Jifei Song, Arthur Moreau, Michal Nazarczuk, Sibi Catley-Chandar, Helisa Dhamo, Eduardo Perez-Pellitero

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.

Novel View Synthesis

HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting

no code implementations5 Dec 2023 Helisa Dhamo, Yinyu Nie, Arthur Moreau, Jifei Song, Richard Shaw, Yiren Zhou, Eduardo Pérez-Pellitero

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

1 code implementation28 Nov 2023 Arthur Moreau, Jifei Song, Helisa Dhamo, Richard Shaw, Yiren Zhou, Eduardo Pérez-Pellitero

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

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

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).

Image Restoration Vocal Bursts Intensity Prediction

HDR Reconstruction from Bracketed Exposures and Events

no code implementations28 Mar 2022 Richard Shaw, Sibi Catley-Chandar, Ales Leonardis, Eduardo Perez-Pellitero

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.

HDR Reconstruction

Acquisition-invariant brain MRI segmentation with informative uncertainties

no code implementations7 Nov 2021 Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak, Sebastien Ourselin, M Jorge Cardoso

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.

MRI segmentation Segmentation

The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties

no code implementations4 Nov 2021 Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak, Sebastien Ourselin, M Jorge Cardoso

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.

Brain Segmentation

A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation

no code implementations6 Sep 2021 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso, Hugh G. Pemberton

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.

MRI segmentation Segmentation

Estimating MRI Image Quality via Image Reconstruction Uncertainty

no code implementations21 Jun 2021 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso

Thus, we argue that quality control for visual assessment cannot be equated to quality control for algorithmic processing.

Image Quality Assessment Image Reconstruction

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

no code implementations MIDL 2019 Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw, Mark Graham, Parashkev Nachev, Sebastien Ourselin, Carole H. Sudre, M. Jorge Cardoso

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

no code implementations MIDL 2019 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso

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

no code implementations26 Apr 2013 Leon Derczynski, Richard Shaw, Ben Solway, Jun Wang

Question answering involves developing methods to extract useful information from large collections of documents.

Information Retrieval Question Answering +1

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