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We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions.
#2 best model for 3D Reconstruction on Scan2CAD
To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of in-the-wild face images, without collecting 3D face scans.
As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, e. g., model fitting, image synthesis.
SOTA for Face Alignment on AFLW2000
This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
We introduce a novel method to obtain high-quality 3D reconstructions from consumer RGB-D sensors.