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chenhsuanlin avatar

chenhsuanlin/signed-distance-SRN

0
View on GitHub↗
129 stars·17 forks·Python·MIT·20 views

Signed Distance SRN

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images 🎯 (NeurIPS 2020)

Features

  • Meta-Learning - Learning 3D implicit representations from single-image supervision via meta-learning.
  • Neural Rendering - Training scene representation networks using single-view observations.

Star history

Star history chart for chenhsuanlin/signed-distance-srnStar history chart for chenhsuanlin/signed-distance-srn

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Signed Distance SRN

These projects share indexed features with Signed Distance SRN. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • bmild/nerfbmild avatar

    bmild/nerf

    10,902View on GitHub↗

    This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of two-dimensional images and camera poses. It functions as a volumetric rendering engine and scene synthesizer that optimizes neural representations of spatial volumes to generate novel views of complex 3D scenes. The system implements a coordinate encoding system that transforms spatial coordinates into high-dimensional space to capture high-frequency geometric details. It also includes a neural mesh extractor that converts trained radiance fields into triangle meshes via march

    Jupyter Notebook
    View on GitHub↗10,902
  • hustvl/4dgaussianshustvl avatar

    hustvl/4DGaussians

    3,783View on GitHub↗

    4DGaussians is a research library and neural rendering engine designed for reconstructing and rendering dynamic three-dimensional scenes. It represents moving environments as a collection of Gaussian primitives that evolve in position and appearance over a temporal dimension. The framework utilizes neural deformation fields to predict spatial offsets and rotations for static point representations, simulating complex motion over time. It further employs temporal basis decomposition to encode motion trajectories into learned functions, compressing dynamic scene data while maintaining smooth tra

    Jupyter Notebook3dcomputer-visioncvpr2024
    View on GitHub↗3,783
  • skjack/ltwskJack avatar

    skJack/LTW

    61View on GitHub↗

    This code belongs to the "Domain General Face Forgery Detection by Learning to Weight" Before run the code, you should run ` $ pip install -r requirements.txt first. This code is mainly focus on the GCD benchamrks, which contain three deepfake dataset: Faceforensics++,CELEB-DF,DFDC (version…

    Python
    View on GitHub↗61

Frequently asked questions

What does chenhsuanlin/signed-distance-srn do?

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images 🎯 (NeurIPS 2020)

What are the main features of chenhsuanlin/signed-distance-srn?

The main features of chenhsuanlin/signed-distance-srn are: Meta-Learning, Neural Rendering.

Which projects share features with chenhsuanlin/signed-distance-srn?

Projects with overlapping indexed features include: bmild/nerf — This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of… hustvl/4dgaussians — 4DGaussians is a research library and neural rendering engine designed for reconstructing and rendering dynamic… skjack/ltw — This code belongs to the "Domain General Face Forgery Detection by Learning to Weight" Before run the code, you should…