Surface Reconstruction
186 papers with code • 2 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Surface Reconstruction
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Libraries
Use these libraries to find Surface Reconstruction models and implementations
OmniObject3D
SceneNet
Stanford-ORB
OMMO
P2S
VIRDO Dataset
ANIM
SynFoot
Most implemented papers
Point2Skeleton: Learning Skeletal Representations from Point Clouds
clinplayer/Point2Skeleton
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CVPR 2021
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds.
Neural RGB-D Surface Reconstruction
dazinovic/neural-rgbd-surface-reconstruction
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CVPR 2022
Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR.
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
autonomousvision/unisurf
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ICCV 2021
At the same time, neural radiance fields have revolutionized novel view synthesis.
Score-Based Point Cloud Denoising
luost26/score-denoise
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ICCV 2021
Since $p * n$ is unknown at test-time, and we only need the score (i. e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of $p * n$ given only noisy point clouds as input.
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.
Extracting Triangular 3D Models, Materials, and Lighting From Images
NVlabs/nvdiffrec
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CVPR 2022
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
Neural Dual Contouring
czq142857/NDC
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We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC).
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds.
LION: Latent Point Diffusion Models for 3D Shape Generation
nv-tlabs/LION
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To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes.
Neural Vector Fields: Implicit Representation by Explicit Learning
Deep neural networks (DNNs) are widely applied for nowadays 3D surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp templates explicitly by moving vertices or represent 3D surfaces implicitly as signed or unsigned distance functions.
