Surface Reconstruction
256 papers with code • 3 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Surface Reconstruction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Surface Reconstruction models and implementations
OmniObject3D
SceneNet
Stanford-ORB
OMMO
P2S
Foot3D
SynFoot
ANIM
VIRDO Dataset
Most implemented papers
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
Totoro97/NeuS
•
•
NeurIPS 2021
In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation.
Patch-based Progressive 3D Point Set Upsampling
yifita/3PU_pytorch
•
•
CVPR 2019
We present a detail-driven deep neural network for point set upsampling.
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
lioryariv/idr
•
•
NeurIPS 2020
In this work we address the challenging problem of multiview 3D surface reconstruction.
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
zju3dv/NeuralRecon
•
•
CVPR 2021
We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video.
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
When compared to existing photorealistic 3D datasets such as Replica, MP3D, Gibson, and ScanNet, images rendered from HM3D have 20 - 85% higher visual fidelity w. r. t.
NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction
Recent methods for neural surface representation and rendering, for example NeuS, have demonstrated the remarkably high-quality reconstruction of static scenes.
Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
Controlling Neural Level Sets
In turn, the sample network can be used to incorporate the level set samples into a loss function of interest.
HumanMeshNet: Polygonal Mesh Recovery of Humans
3D Human Body Reconstruction from a monocular image is an important problem in computer vision with applications in virtual and augmented reality platforms, animation industry, en-commerce domain, etc.
Surface Reconstruction from 3D Line Segments
In man-made environments such as indoor scenes, when point-based 3D reconstruction fails due to the lack of texture, lines can still be detected and used to support surfaces.
