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Greatest papers with code

Learning Implicit Fields for Generative Shape Modeling

CVPR 2019 czq142857/implicit-decoder

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.

3D RECONSTRUCTION 3D SHAPE REPRESENTATION REPRESENTATION LEARNING SINGLE-VIEW 3D RECONSTRUCTION

SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization

CVPR 2020 YueJiang-nj/CVPR2020-SDFDiff

We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDF).

3D RECONSTRUCTION SINGLE-VIEW 3D RECONSTRUCTION

Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction

17 Jun 2020zhou13/symmetrynet

In this work, we focus on object-level 3D reconstruction and present a geometry-based end-to-end deep learning framework that first detects the mirror plane of reflection symmetry that commonly exists in man-made objects and then predicts depth maps by finding the intra-image pixel-wise correspondence of the symmetry.

3D RECONSTRUCTION SINGLE-VIEW 3D RECONSTRUCTION

PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes

CVPR 2020 ChrisWu1997/PQ-NET

We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.

3D RECONSTRUCTION SINGLE-VIEW 3D RECONSTRUCTION

Learning to Recover 3D Scene Shape from a Single Image

17 Dec 2020aim-uofa/depth

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.

3D SCENE RECONSTRUCTION MONOCULAR DEPTH ESTIMATION SINGLE-VIEW 3D RECONSTRUCTION