Image: Gwak et al
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We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras.
Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene.
Ranked #1 on 3D Reconstruction on ScanNet
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks.
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).
Ranked #4 on 3D Reconstruction on Data3D−R2N2
We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild.
We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM).
Representing the reconstruction volumetrically as a TSDF leads to most of the simplicity and efficiency that can be achieved with GPU implementations of these systems.
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.