Search Results for author:
Found 26 papers, 11 papers with code
A Framework for the Volumetric Integration of Depth Images
Along with the framework we also provide a set of components for scalable reconstruction: two implementations of camera trackers, based on RGB data and on depth data, two representations of the 3D volumetric data, a dense volume and one based on hashes of subblocks, and an optional module for swapping subblocks in and out of the typically limited GPU memory.
Exploiting Uncertainty in Regression Forests for Accurate Camera Relocalization
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure.
Joint Object-Material Category Segmentation from Audio-Visual Cues
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.
Learning to Navigate the Energy Landscape
We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization.
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation.
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems
Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision.
Low Compute and Fully Parallel Computer Vision With HashMatch
Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.
Backtracking Regression Forests for Accurate Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection.
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
Ranked #2 on
Stereo Depth Estimation
on sceneflow
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Ranked #2 on
6D Pose Estimation using RGBD
on CAMERA25
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.
ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation
To incorporate this uncertainty measure, we introduce a new viewpoint entropy formulation, which is the basis of our active learning strategy.
SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans
We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion.
FastNeRF: High-Fidelity Neural Rendering at 200FPS
Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints.
Learning to Fit Morphable Models
A common first step in systems that tackle these problems is to regress the parameters of the parametric model directly from the input data.
3D face reconstruction with dense landmarks
By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild.
Ranked #1 on
3D Face Reconstruction
on Florence
(RMSE Indoor metric)
VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations
The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time.
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet.
Ranked #2 on
Synthetic Face Recognition
on CPLFW
(Accuracy metric)
X-Avatar: Expressive Human Avatars
Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data.
Dynamic Point Fields
In this work, we present a dynamic point field model that combines the representational benefits of explicit point-based graphics with implicit deformation networks to allow efficient modeling of non-rigid 3D surfaces.
BlendFields: Few-Shot Example-Driven Facial Modeling
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance.
360° Volumetric Portrait Avatar
In contrast to this, we propose a template-based tracking of the torso, head and facial expressions which allows us to cover the appearance of a human subject from all sides.
