maximevandegar/papers-in-100-lines-of-code

How maximevandegar/papers-in-100-lines-of-code works

This repository is a catalog of compact, mostly independent implementations of research papers—not one integrated application. The user-facing workflow is to choose a paper implementation, provide its expected data or inputs, run its script, and inspect generated predictions, renders, or other results. The implementations span image and 3D reconstruction, neural rendering, diffusion and other generative models, reinforcement learning, and foundational learning methods. The README establishes the catalog’s purpose; sampled source shows some model and rendering internals. Most scripts were not sampled, so their exact runtime wiring is not asserted.

Main entry points: pixels_to_gaussians.py Pixel Gaussians · splatter_image.py Splatter Image · gaussian_splatting.py Gaussian renderer · unet.py DDIM U-Net

Components 24

Reconstruction and rendering

Pixel Gaussians A_Pixel_Is_Worth_More_Than_One_3D_Gaussians_in_Single_View_3D_Reconstruction/pixels_to_gaussians.py
Splatter Image Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/splatter_image.py
Gaussian renderer Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/gaussian_splatting.py
3D Gaussian Splatting 3D_Gaussian_Splatting_for_Real_Time_Radiance_Field_Rendering/3dgs.py
Speedy Splat Speedy_Splat_Fast_3D_Gaussian_Splatting_with_Sparse_Pixels_and_Sparse_Primitives/speedy_splat.py
Volumetric rendering Differentiable_Volumetric_Rendering_Learning_Implicit_3D_Representations_without_3D_Supervision/dvr.py
NeRF NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis/nerf.py
PixelNeRF PixelNeRF_Neural_Radiance_Fields_from_One_or_Few_Images/pixelnerf.py

Generative models

GAN Generative_Adversarial_Networks/GANs.py
Conditional GAN Conditional_Generative_Adversarial_Nets/cgan.py
Latent diffusion model High_Resolution_Image_Synthesis_with_Latent_Diffusion_Models/model.py
Diffusion models Denoising_Diffusion_Probabilistic_Models/diffusion_models.py
DDIM U-Net Denoising_Diffusion_Implicit_Models/unet.py
DreamBooth DreamBooth_Fine_Tuning_Text_to_Image_Diffusion_Models_for_Subject_Driven_Generation/dreambooth.py
Folding diffusion Protein_Structure_Generation_via_Folding_Diffusion/foldingdiff.py
Real NVP Density_Estimation_Using_Real_NVP/real_nvp.py

Learning methods

Adam optimizer Adam_a_Method_For_Stochastic_Optimization/adam.py
ResNet Deep_Residual_Learning_for_Image_Recognition/resnet.py
MAML Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks/maml.py
RMS normalization Root_Mean_Square_Layer_Normalization/rmsnorm.py

Reinforcement learning

DQN Playing_Atari_with_Deep_Reinforcement_Learning/dqn.py
Double DQN Deep_Reinforcement_Learning_with_Double_Q_learning/ddqn.py
PPO Proximal_Policy_Optimization_Algorithms/ppo.py

Other components

Research user

Connections 17

Research user → Pixel Gaussians: chooses implementation
Research user → Splatter Image: chooses implementation
Research user → 3D Gaussian Splatting: chooses implementation
Research user → Volumetric rendering: chooses implementation
Research user → NeRF: chooses implementation
Research user → GAN: chooses implementation
Research user → Latent diffusion model: chooses implementation
Research user → Folding diffusion: chooses implementation
Research user → Real NVP: chooses implementation
Research user → Adam optimizer: chooses implementation
Research user → MAML: chooses implementation
Research user → DQN: chooses implementation
Research user → PPO: chooses implementation
Pixel Gaussians → Gaussian renderer: imports renderer A_Pixel_Is_Worth_More_Than_One_3D_Gaussians_in_Single_View_3D_Reconstruction/pixels_to_gaussians.py
Splatter Image → Gaussian renderer: uses renderer Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/splatter_image.py
Pixel Gaussians → DDIM U-Net: imports U-Net A_Pixel_Is_Worth_More_Than_One_3D_Gaussians_in_Single_View_3D_Reconstruction/pixels_to_gaussians.py
Splatter Image → DDIM U-Net: imports U-Net Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/splatter_image.py

Drawn by GitDiagram on November 12, 2026 from the repository's file tree, README and source files. Read as Markdown ·