PET-NeuS
sdfstudio
PET-NeuS | sdfstudio | |
---|---|---|
1 | 1 | |
250 | 1,870 | |
- | 2.1% | |
1.5 | 6.2 | |
12 days ago | 7 months ago | |
Python | Python | |
- | Apache License 2.0 |
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PET-NeuS
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PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces
A signed distance function (SDF) parametrized by an MLP is a common ingredient of neural surface reconstruction. We build on the successful recent method NeuS to extend it by three new components. The first component is to borrow the tri-plane representation from EG3D and represent signed distance fields as a mixture of tri-planes and MLPs instead of representing it with MLPs only. Using tri-planes leads to a more expressive data structure but will also introduce noise in the reconstructed surface. The second component is to use a new type of positional encoding with learnable weights to combat noise in the reconstruction process. We divide the features in the tri-plane into multiple frequency scales and modulate them with sin and cos functions of different frequencies. The third component is to use learnable convolution operations on the tri-plane features using self-attention convolution to produce features with different frequency bands. The experiments show that PET-NeuS achieves high-fidelity surface reconstruction on standard datasets. Following previous work and using the Chamfer metric as the most important way to measure surface reconstruction quality, we are able to improve upon the NeuS baseline by 57% on Nerf-synthetic (0.84 compared to 1.97) and by 15.5% on DTU (0.71 compared to 0.84). The qualitative evaluation reveals how our method can better control the interference of high-frequency noise. Code available at \url{https://github.com/yiqun-wang/PET-NeuS}.
sdfstudio
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3D digital twin of Zurich created using NeRF in 3.5 hours
Hey, are you referring to the application or the digital twin? The application in itself is closed-source but you can probably achieve something similar with https://github.com/autonomousvision/sdfstudio
What are some alternatives?
DicomToMesh - A command line tool to transform a DICOM volume into a 3d surface mesh (obj, stl or ply). Several mesh processing routines can be enabled, such as mesh reduction, smoothing or cleaning. Works on Linux, OSX and Windows.
nerfstudio - A collaboration friendly studio for NeRFs
smriprep - Structural MRI PREProcessing (sMRIPrep) workflows for NIPreps (NeuroImaging PREProcessing tools)
kaolin-wisp - NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD).
giraffe - This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
nerfacc - A General NeRF Acceleration Toolbox in PyTorch.
PolyFit - Polygonal Surface Reconstruction from Point Clouds
ocf-models - OCF https://openconnectivity.org/ models in SDF format
awesome-NeRF - A curated list of awesome neural radiance fields (NeRF) papers.
CIPS-3D - 3D-aware GANs based on NeRF (arXiv).
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
simple_photogrammetry_gui