87 citations · 344 across the 19 of their papers we have counts for
21 papers · 1 filter
BlendFields: Few-Shot Example-Driven Facial Modeling
Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi +5
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven…
Dynamic Point Fields
Sergey Prokudin, Qianli Ma, Maxime Raafat +2
Recent years have witnessed significant progress in the field of neural surface reconstruction. While the extensive focus was put on volumetric and implicit approaches, a number of…
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis +5
State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale datasets…
3D face reconstruction with dense landmarks
Erroll Wood, Tadas Baltrusaitis, Charlie Hewitt +11
Landmarks often play a key role in face analysis, but many aspects of identity or expression cannot be represented by sparse landmarks alone. Thus, in order to reconstruct faces mo…
Learning to Fit Morphable Models
Vasileios Choutas, Federica Bogo, Jingjing Shen +1
Fitting parametric models of human bodies, hands or faces to sparse input signals in an accurate, robust, and fast manner has the promise of significantly improving immersion in AR…
FastNeRF: High-Fidelity Neural Rendering at 200FPS
Stephan J. Garbin, Marek Kowalski, Matthew Johnson +2
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.…
SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans
Angela Dai, Yawar Siddiqui, Justus Thies +2
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 s…
ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation
Yawar Siddiqui, Julien Valentin, Matthias Nießner
We propose ViewAL, a novel active learning strategy for semantic segmentation that exploits viewpoint consistency in multi-view datasets. Our core idea is that inconsistencies in m…
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Srinath Sridhar, Davis Rempe, Julien Valentin +2
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances. Most approaches for…
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
He Wang, Srinath Sridhar, Jingwei Huang +3
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem as…
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang +14
Motivated by augmented and virtual reality applications such as telepresence, there has been a recent focus in real-time performance capture of humans under motion. However, given…
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord +4
Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, o…
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
Sameh Khamis, Sean Fanello, Christoph Rhemann +3
This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing high-quality, edge-preserved…
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
Yinda Zhang, Sameh Khamis, Christoph Rhemann +7
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it p…
Backtracking Regression Forests for Accurate Camera Relocalization
Lili Meng, Jianhui Chen, Frederick Tung +3
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. Recent…
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Lili Meng, Frederick Tung, James J. Little +2
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. Recent…
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord +3
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techn…
Learning to Navigate the Energy Landscape
Julien Valentin, Angela Dai, Matthias Nießner +4
In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimizati…
Joint Object-Material Category Segmentation from Audio-Visual Cues
Anurag Arnab, Michael Sapienza, Stuart Golodetz +4
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 d…
SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes
Stuart Golodetz, Michael Sapienza, Julien P. C. Valentin +9
We present an open-source, real-time implementation of SemanticPaint, a system for geometric reconstruction, object-class segmentation and learning of 3D scenes. Using our system,…
A Framework for the Volumetric Integration of Depth Images
Victor Adrian Prisacariu, Olaf Kähler, Ming Ming Cheng +5
Volumetric models have become a popular representation for 3D scenes in recent years. One of the breakthroughs leading to their popularity was KinectFusion, where the focus is on 3…