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Opt
Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes. The mathematical descriptions of theseExpand
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
TLDR
This work introduces ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations, and shows that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks. Expand
FaceForensics++: Learning to Detect Manipulated Facial Images
TLDR
This paper proposes an automated benchmark for facial manipulation detection, and shows that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers. Expand
Matterport3D: Learning from RGB-D Data in Indoor Environments
TLDR
Matterport3D is introduced, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400RGB-D images of 90 building-scale scenes that enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification. Expand
Face2Face: real-time face capture and reenactment of RGB videos
TLDR
Face2Face addresses the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling and convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. Expand
Volumetric and Multi-view CNNs for Object Classification on 3D Data
TLDR
This paper introduces two distinct network architectures of volumetric CNNs and examines multi-view CNNs, providing a better understanding of the space of methods available for object classification on 3D data. Expand
Real-time 3D reconstruction at scale using voxel hashing
TLDR
An online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Expand
BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration
TLDR
This work systematically addresses issues with a novel, real-time, end-to-end reconstruction framework, which outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness. Expand
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
TLDR
3DMatch is presented, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data that consistently outperforms other state-of-the-art approaches by a significant margin. Expand
Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis
TLDR
A data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis and a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers. Expand
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