Neural Radiance Fields (NeRF)

Learn the pinhole camera model for NeRF, using backward ray tracing to cast rays from image pixels through the center into the scene and compute color.

Visualize camera rays in 3d by plotting origin and ray directions over time, creating a 3d ray plot and a simple simulator to render an image from a 3d model.

Design a simple ray tracer to render a sphere by solving a quadratic intersection and assigning colors, then produce a 2D image.

Explore volumetric rendering for neural radiance fields (NeRF), comparing tri-mesh representations with density-color volumes, and learn ray sampling, density integration, and accumulated transmittance for rendering.

Implement a volumetric rendering function for neural radiance fields using ray sampling and bin-based integration. Query color and density along sampled points and accumulate transmittance to produce final pixel colors.

Learn differentiable rendering and optimization for neural radiance fields (NeRF), using gradients and gradient descent to match a target image by adjusting color parameters of a 3D volumetric model.

Apply a 3x3 rotation inside a 4x4 camera-to-world matrix to transform ray directions for neural radiance field reconstruction.

This NeRF lecture builds a voxel reconstruction pipeline, loading camera and dataset, moving from a simple sphere to voxel and neural networks for richer scene reconstruction.

Master camera setup, dataset construction, and alpha blending concepts for neural radiance fields, with PyTorch data loaders handling origin, direction, and target pixel values.

Implement and optimize volumetric rendering in NeRF by integrating a rendering function, sampling voxels with bin-based space partitioning and uniform sampling, and comparing single versus dual-model strategies for density estimation.

Design and implement a voxel-based 3d model for NeRF rendering in PyTorch, building a voxel grid, storing color and density, and wiring forward and intersect methods for rendering.

Implement a supervised learning training loop for NeRF by wiring a model, optimizer, and scheduler, feeding batches, moving data to device, computing loss, and performing backpropagation across epochs.

Explore white background regularization in NeRF training to push empty space density toward zero, improving multi-view consistency and background handling.

Learn how mode collapse can arise on synthetic data in NeRF and how to mitigate it with center-of-the-image warm-up and activation choices such as soft plus or trunk exponential.

Explore neural radiance fields, comparing voxel grids with neural networks for 3d reconstruction, addressing mode collapse, multi-view consistency, view-dependent color, and the role of positional encoding.

Implement the NeRF architecture by building sequential blocks with positional encoding, skip connections, and a direction-conditioned density and color output.

Learn how to implement positional encoding for NeRF by computing sine and cosine features of x, y, z, concatenating them with the input, and integrating direction handling.

Shows vanilla NeRF results after one epoch with rapid loss drop and gradual detail refinement. Discusses memory constraints in rendering and plans for novel view synthesis and 3D reconstruction.

Learn how to generate novel viewpoints with NeRF by tracing testing rays, batching and chunking to manage CUDA memory, and render images and animations from new camera poses.

Extract a three-dimensional mesh from a nerf density field by building a voxel grid, querying densities, and applying marching cubes with a tunable threshold for clean surfaces.

Explore color extraction for neural radiance fields by turning a mesh into normal-based color queries, address occlusion issues, and compare triangle-normal and vertex-normal methods for color prediction.

Explore NeRF color extraction method 2, projecting 3D points to image space using camera parameters, interpolating pixel colors across views and weighting by training-view opacity to reduce occlusion.

Review four NeRF papers, starting with positional encoding and why it matters, then explore methods that reduce training and inference time, including single-image training, with flexible order.

Fourier features and positional encoding enable neural networks to learn high-frequency details in low-dimensional spaces, improving image regression and showing fast low-frequency learning with slower high-frequency convergence.

Explore implicit neural representations with periodic sine activations in siren layers for NeRF, capturing high-frequency details and outperforming traditional activations, with careful weight initialization.

Multiplicative filter networks provide a simple parameterization to learn high-frequency signals in neural radiance fields, outperforming positional encodings in detail; the lecture notes highlight easy implementation with concise code.

Examine learned initializations for fast optimization of coordinate-based neural representations (NeRF) using meta-learning and amortization, enabling single-image training and improved 3D consistency.

The GRF approach learns a general radiance field from diverse objects and scenes to enable fast 3d reconstruction and new viewpoints for unseen categories without training during inference.

PixelNeRF renders new viewpoints from one or few images and learns the 3D shape of the scene. It compares to NeRF with limited views, offering fast inference and no fine-tuning.

Giraffe represents scenes as compositional generative neural feature fields, using per-object latent variables to control shape, appearance, and position, enabling flexible scene composition through weighted feature fusion.

Invert neural radiance fields to estimate camera parameters from images, using a fixed nerf model and MSG loss to refine pose, with pixel nerf and call map initialization.

Learn how NeRFs can be trained directly from phone images without known camera parameters, using joint optimization or a two-stage approach to avoid local minima and improve results.

Learn AutoInt, a method that speeds volume rendering by reformulating integration as learning a derivative. Use automatic differentiation to enforce 3d consistency, enabling a single ray per pixel at inference.

KiloNeRF speeds up neural radiance fields with thousands of tiny MLPs on a voxel grid. Train a full NeRF, then distill into tiny networks with no loss in performance.

Light field networks enable single-evaluation rendering per pixel by conditioning a neural radiance field on a scene latent code learned through meta learning and auto encoding, enabling fast inference.

FastNeRF speeds neural rendering by caching position and direction features in a 3D grid, decoupling position from direction to reach up to 200 fps.

Explore squeeze NeRF, a memory-efficient extension of fast NeRF that factorizes position and direction to reduce complexity from cubic to quadratic and enable small on-device caches.

PlenOctrees enable real-time rendering of neural radiance fields by accelerating inference to about 150 fps, empowering e-commerce visualization of 3D objects with high performance and memory efficiency.

Explore how deep SDF uses neural networks to represent scenes implicitly via a signed distance function, where zero level sets define surfaces and enable mesh extraction.

NeuS learns neural implicit surfaces by volume rendering with an sdf predictor and a color predictor, delivering sharper meshes, accurate depth, and a user-friendly code base.

Explore Unisurf, a method that unifies neural implicit surfaces and radiance fields for joint surface and volumetric rendering, using alpha values and root-finding for surface extraction.

Plenoxels enable radiance fields without neural networks by using a sparse voxel grid with color harmonics, enabling direct color queries and up to 100x faster convergence.

Explore instant neural graphics primitives for neRF using a multiresolution hash encoding. See how hash-based voxel features enable fast, lightweight color and density predictions with interpolation and collision handling.

Explore the 2020 state of the art in neural rendering, including neural networks for renderings, NeRFs, and GAN-based approaches, with applications in 3D reconstruction and industry use.

Explore advances in neural rendering with NeRF, including 2D and 3D neural ordering, relighting with GANs, and the shift from explicit meshes to volumetric and implicit representations.

NeRF++ analyzes neural radiance fields and introduces inverted shape parameterization to separate foreground inside a sphere from background outside it, improving test-view generalization and background modeling.

Explore aliasing in neural radiance fields and how Mip-NeRF uses a multiscale parameterization to model the pixel integral, smoothing edges at low resolutions.

Mip-NeRF 360 uses unbounded, anti-aliased parameterization to concentrate rays near the object while regularizing density toward the scene center, improving background modeling for indoor and outdoor setups.

Learn to calibrate camera parameters for neural radiance fields, including intrinsic and extrinsic parameters, and nonlinear distortion, refining estimates from initial code map results to better model real-life cameras.

DeRF decomposes radiance fields by partitioning space into regions, assigns a local tiny MLP to each region, and combines results with a painter's algorithm for faster, better rendering.

Explore neural sparse voxel fields and occupancy grids to accelerate NeRF training and inference by pruning low-density voxels and sampling only where density exists.

Explore open source GitHub repositories and commercial tools like Luma to capture scenes in 3D, export 3D meshes for game assets, and use an API for no-code NeRF workflows.

Discover beginner-friendly nerf repositories with a PyTorch implementation mirroring the vanilla nerf paper, easy to run, and capable of 3d mesh extraction and Unity import.

Explore NerfStudio, a NeRF pipeline with easy installation, multiple models, training with datasets, data format details, mesh export, and camera pose refinement, plus integrating a custom model into NerfStudio.

Install NerfStudio from GitHub, upgrade pip, and install torch with CUDA to accelerate NeRF training; run vanilla NeRF on the chair dataset and monitor progress.

Explore integrating voxel models into NerfStudio by replacing the vanilla Nerf with a voxel model, configuring voxel fields, and enabling density and color outputs for faster training.

Demonstrates accelerating NeRF training with NerfStudio by scaling voxels, lowering beta, and increasing learning rate; compares coarse and fine voxels, tracks training loss, and discusses multi-resolution sampling.

Explore explicit surface reconstruction and compare it with implicit NeRF approaches. See how SDF Studio builds on the studio, enabling easy transfer and shared configurations for room reconstruction.

Demonstrates how tiny-cuda-nn accelerates training and inference on GPUs. Shows a one-line install, GPU-optimized fully fused operations, and PyTorch integration.

Implement a fully fused neural network to speed up nerf training, swapping PyTorch MLPs for tiny CUDA MLPs, reducing training time by about twofold and exploring encoding options.

Explore instant-ngp, Nvidia's CUDA-based NeRF implementation, its installation challenges, and a Windows interface for viewing models; compare with a no-code PyTorch MVP that simplifies understanding.

Explore a self-contained PyTorch workflow implementing nerf pl and the incident gb paper for neural radiance fields, with training and rendering functions and tiny CUDA optimizations.

Clone the GPL repository, install the required versions of torch scatter and Fuse Atom, and complete the ngp_pl installation to run NeRF on GPUs, including paper space, with minimal setup.

Navigate ngp_pl training for neural radiance fields, resolve cuda and apex compatibility issues, configure north dataset paths and training arguments, and evaluate results with depth rendering and notebook workflows.

Tune the nerf model by adjusting the scale parameter, compare 0.5 vs 4.5 scales, and train quickly to achieve sharp 3d reconstructions with minimal overfitting.

NerfAcc introduces a toolbox for nerf acceleration that enables efficient volumetric rendering by using occupancy grids, sparse voxels, and tiny dnn, reducing vanilla nerf training from days to hours.

Thank learners for completing the NeRF course and invite feedback to improve future updates with recent developments. Encourage reviews, sharing, and talking about it with colleagues and friends.