91 citations · 288 across the 14 of their papers we have counts for
16 papers
LSMAT Least Squares Medial Axis Transform
Daniel Rebain, Baptiste Angles, Julien Valentin +4
The medial axis transform has applications in numerous fields including visualization, computer graphics, and computer vision. Unfortunately, traditional medial axis transformation…
Deep Implicit Volume Compression
Danhang Tang, Saurabh Singh, Philip A. Chou +11
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method re…
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
Hossam Isack, Christian Haene, Cem Keskin +4
We propose a novel efficient and lightweight model for human pose estimation from a single image. Our model is designed to achieve competitive results at a fraction of the number o…
VIPER: Volume Invariant Position-based Elastic Rods
Baptiste Angles, Daniel Rebain, Miles Macklin +8
We extend the formulation of position-based rods to include elastic volumetric deformations. We achieve this by introducing an additional degree of freedom per vertex -- isotropic…
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
Rohit Pandey, Anastasia Tkach, Shuoran Yang +9
Volumetric (4D) performance capture is fundamental for AR/VR content generation. Whereas previous work in 4D performance capture has shown impressive results in studio settings, th…
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…
MIST: Multiple Instance Spatial Transformer Network
Baptiste Angles, Yuhe Jin, Simon Kornblith +2
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervisio…
SplineNets: Continuous Neural Decision Graphs
Cem Keskin, Shahram Izadi
We present SplineNets, a practical and novel approach for using conditioning in convolutional neural networks (CNNs). SplineNets are continuous generalizations of neural decision g…
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…
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
Angela Dai, Matthias Nießner, Michael Zollhöfer +2
Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic applications. However, scalability brings challenges of drift in pose estimation, int…
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…
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
Yinda Zhang, Mingru Bai, Pushmeet Kohli +2
While deep neural networks have led to human-level performance on computer vision tasks, they have yet to demonstrate similar gains for holistic scene understanding. In particular,…
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 Light Transport Model for Mitigating Multipath Interference in TOF Sensors
Nikhil Naik, Achuta Kadambi, Christoph Rhemann +3
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics. However, the depth images obt…