Volume Rendering and Neural Radiance Field: Assigment 3 (16-889)

Name: Shefali Srivastava
Andrew ID: shefalis

Late Days:

image

0. Setup

0.1 Environment setup

You can use the python environment you’ve set up for past assignments, or re-install it with our environment.yml file:

conda env create -f environment.yml
conda activate l3d

If you do not have Anaconda, you can quickly download it here, or via the command line in with:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod +x Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

0.2 Data

The data for this assignment is provided in the github repo under data/. You do not need to download anything yourself.

1. Differentiable Volume Rendering

In the emission-absorption (EA) model that we covered in class, volumes are described by their appearance (e.g. emission) and geometry (absorption) at every point in 3D space. For part 1 of the assignment, you will implement a Differentiable Renderer for EA volumes, which you will use in parts 2 and 3. Differentiable renderers are extremely useful for 3D learning problems — one reason is because they allow you to optimize scene parameters (i.e. perform inverse rendering) from image supervision only!

1.1. Familiarize yourself with the code structure

There are four major components of our differentiable volume rendering pipeline:

StratifiedSampler provides a method for sampling multiple points along a ray traveling through the scene (also known as raymarching). Together, a sampler and a renderer describe a rendering pipeline. Like traditional graphics pipelines, this rendering procedure is independent of the scene and camera.

The scene, sampler, and renderer are all packaged together under the Model class in main.py. In particular the Model‘s forward method invokes a VolumeRenderer instance with a sampling strategy and volume as input.

Also, take a look at the RayBundle class in ray_utils.py, which provides a convenient wrapper around several per-ray inputs to the volume rendering procedure.

1.2. Outline of tasks

In order to perform rendering, you will implement the following routines:

  1. Ray sampling from cameras: you will fill out methods in ray_utils.py to generate world space rays from a particular camera.
  2. Point sampling along rays: you will fill out the StratifiedSampler class to generate sample points along each world space ray
  3. Rendering: you will fill out the VolumeRenderer class to evaluate a volume function at each sample point along a ray, and aggregate these evaluations to perform rendering.

1.3. Ray sampling (10 points)

Take a look at the render_images function in main.py. It loops through a set of cameras, generates rays for each pixel on a camera, and renders these rays using a Model instance.

Implementation

Your first task is to implement:

  1. get_pixels_from_image in ray_utils.py and
  2. get_rays_from_pixels in ray_utils.py

which are used in render_images:

xy_grid = get_pixels_from_image(image_size, camera) # TODO: implement in ray_utils.py
ray_bundle = get_rays_from_pixels(xy_grid, camera) # TODO: implement in ray_utils.py

The get_pixels_from_image method generates pixel coordinates, ranging from [-1, 1] for each pixel in an image. The get_rays_from_pixels method generates rays for each pixel, by mapping from a camera’s Normalized Device Coordinate (NDC) Space into world space.

Visualization

You can run the code for part 1 with:

python main.py --config-name=box

Once you have implemented these methods, verify that your output matches the TA output by visualizing both xy_grid and rays with the vis_grid and vis_rays functions in the render_images function in main.py. By default, the above command will crash and return an error. However, it should reach your visualization code before it does. The outputs of grid/ray visualization should look like this:

Grid Rays

ANSWER:

Q1.3 XY Grid Q1.3 Rays

1.4. Point sampling (10 points)

Implementation

Your next task is to fill out StratifiedSampler in sampler.py. Implement the forward method, which:

  1. Generates a set of distances between near and far and
  2. Uses these distances to sample points offset from ray origins (RayBundle.origins) along ray directions (RayBundle.directions).
  3. Stores the distances and sample points in RayBundle.sample_points and RayBundle.sample_lengths

Visualization

Once you have done this, use the render_points method in render_functions.py in order to visualize the point samples from the first camera. They should look like this:

Sample points

ANSWER:

Q1.4 Points

1.5. Volume rendering (30 points)

Finally, we can implement volume rendering! With the configs/box.yaml configuration, we provide you with an SDFVolume instance describing a box. You can check out the code for this function in implicit.py, which converts a signed distance function into a volume. If you want, you can even implement your own SDFVolume classes by creating new signed distance function class, and adding it to sdf_dict in implicit.py. Take a look at this great web page for formulas for some simple/complex SDFs.

Implementation

You will implement

  1. VolumeRenderer._compute_weights and
  2. VolumeRenderer._aggregate.
  3. You will also modify the VolumeRenderer.forward method to render a depth map in addition to color from a volume

From each volume evaluation you will get both volume density, and a color (feature):

# Call implicit function with sample points
implicit_output = implicit_fn(cur_ray_bundle)
density = implicit_output['density']
feature = implicit_output['feature']

You’ll then use the following equation to render color along a ray:

Spiral Rendering of Part 1

where σ is density, Δt is the length of current ray segment, and L_e is color:

Spiral Rendering of Part 1

Compute the weights T * (1 - exp(-σ * Δt)) in VolumeRenderer._compute_weights, and perform the summation in VolumeRenderer._aggregate. Note that for the first segment T = 1.

Use weights, and aggregation function to render color and depth (stored in RayBundle.sample_lengths).

Visualization

By default, your results will be written out to images/part_1.gif. Provide a visualization of the depth in your write-up.

Spiral Rendering of Part 1 Spiral Rendering of Part 1

ANSWER:

Part 1 GIF Part 1 Visualisation

2. Optimizing a basic implicit volume

2.1. Random ray sampling (5 points)

Since you have now implemented a differentiable volume renderer, we can use it to optimize the parameters of a volume! We have provided a basic training loop in the train method in main.py.

Depending on how many sample points we take for each ray, volume rendering can consume a lot of memory on the GPU (especially during the backward pass of gradient descent). Because of this, it usually makes sense to sample a subset of rays from a full image for each training iteration. In order to do this, implement the get_random_pixels_from_image method in ray_utils.py, invoked here:

xy_grid = get_random_pixels_from_image(cfg.training.batch_size, image_size, camera) # TODO: implement in ray_utils.py

2.2. Loss and training (5 points)

Replace the loss in train

loss = None

with mean squared error between the predicted colors and ground truth colors rgb_gt.

Once you’ve done this, you can run train a model with

python main.py --config-name=train_box

This will optimize the position and side lengths of a box, given a few ground truth images with known camera poses (in the data folder). Report the center of the box, and the side lengths of the box after training, rounded to the nearest 1/100 decimal place.

ANSWER:

Box center: (0.25, 0.25, -0.00)

Box side lengths: (2.00, 1.50, 1.50)

2.3. Visualization

The code renders a spiral sequence of the optimized volume in images/part_2.gif. Compare this gif to the one below, and attach it in your write-up:

Spiral Rendering of Part 2

ANSWER:

Part 2 GIF

3. Optimizing a Neural Radiance Field (NeRF) (30 points)

In this part, you will implement an implicit volume as a Multi-Layer Perceptron (MLP) in the NeuraRadianceField class in implicit.py. This MLP should map 3D position to volume density and color. Specifically:

  1. Your MLP should take in a RayBundle object in its forward method, and produce color and density for each sample point in the RayBundle.
  2. You should also fill out the loss in train_nerf in the main.py file.

You will then use this implicit volume to optimize a scene from a set of RGB images. We have implemented data loading, training, checkpointing for you, but this part will still require you to do a bit more legwork than for Parts 1 and 2. You will have to write the code for the MLP yourself — feel free to reference the NeRF paper, though you should not directly copy code from an external repository.

Implementation

Here are a few things to note:

  1. For now, your NeRF MLP does not need to handle view dependence, and can solely depend on 3D position.
  2. You should use the ReLU activation to map the first network output to density (to ensure that density is non-negative)
  3. You should use the Sigmoid activation to map the remaining raw network outputs to color
  4. You can use Positional Encoding of the input to the network to achieve higher quality. We provide an implementation of positional encoding in the HarmonicEmbedding class in implicit.py.

Visualization

You can train a NeRF on the lego bulldozer dataset with

python main.py --config-name=nerf_lego

This will create a NeRF with the NeuralRadianceField class in implicit.py, and use it as the implicit_fn in VolumeRenderer. It will also train a NeRF for 250 epochs on 128x128 images.

Feel free to modify the experimental settings in configs/nerf_lego.yaml — though the current settings should allow you to train a NeRF on low-resolution inputs in a reasonable amount of time. After training, a spiral rendering will be written to images/part_3.gif. Report your results. It should look something like this:

Spiral Rendering of Part 3

ANSWER:

Part 3 GIF

4. NeRF Extras (Choose at least one! More than one is extra credit)

4.1 View Dependence (10 pts)

Add view dependence to your NeRF model! Specifically, make it so that emission can vary with viewing direction. You can NeRF or other papers for how to do this effectively — if you’re not careful, your network may overfit to the training images. Discuss the trade-offs between increased view dependence and generalization quality.

ANSWER:

NeRF uses only positional encoding to calculate the density. Direction is only used for predicting rgb color information. Using direction encodings while predicting the color should enable the network to capture view dependent effects such as specular effects (visible while rendering shiny surfaces). If we pass the direction input early in the network, the network might overfit to generate good reconstructions only for the training input views.

For low-resolution nerf lego task, adding view dependence didn’t produce any signifcant improvements. On looking closely, we can see that view dependence improves the quality of the generated rendering and we can see view dependent color effects at the top and bottom of the vehicle. Without the use of view dependence, we also see some reflection like artifacts being generated at the bottom of the truck. By learning view direction specific colors, the network is able to generalize better to unseen views.

Code:

python main.py --config-name=nerf_lego_dir

Configuration:

configs/nerf_lego_dir.yaml 

seed: 1

type: train_nerf

training:
  num_epochs: 250
  batch_size: 1024
  lr: 0.0005

  lr_scheduler_step_size: 50
  lr_scheduler_gamma: 0.8

  checkpoint_path: ./checkpoints
  checkpoint_interval: 50
  resume: True

  render_interval: 10

data:
  image_size: [128, 128]
  dataset_name: lego

renderer:
  type: volume
  chunk_size: 32768
  white_background: False

sampler:
  type: stratified
  n_pts_per_ray: 128

  min_depth: 2.0
  max_depth: 6.0

implicit_function:
  type: nerf_dir

  n_harmonic_functions_xyz: 6
  n_harmonic_functions_dir: 2
  n_hidden_neurons_xyz: 128
  n_hidden_neurons_dir: 64
  density_noise_std: 0.0
  n_layers_xyz: 6
  append_xyz: [3]

Visualisation:

Part 3 GIF
Naive
Part 4.1 GIF
Directional

4.2 Hierarchical Sampling (10 pts)

NOT ATTEMPTED

NeRF employs two networks: a coarse network and a fine network. During the coarse pass, it uses the coarse network to get an estimate of geometry geometry, and during fine pass uses these geometry estimates for better point sampling for the fine network. Implement this hierarchical point-sampling strategy and discuss trade-offs (speed / quality).

4.3 High Resolution Imagery (10 pts)

Run NeRF on high-res imagery using the nerf_lego_highres.yaml config file. This will take a long time to train – play around with some hyper parameters (point samples per ray, network capacity) and report your results.

ANSWER:

Code:

python main.py --config-name=nerf_lego_highres

Configuration:

configs/nerf_lego_highres.yaml 

seed: 1

type: train_nerf

training:
  num_epochs: 250
  batch_size: 1024
  lr: 0.0005

  lr_scheduler_step_size: 50
  lr_scheduler_gamma: 0.8

  checkpoint_path: ./checkpoints
  checkpoint_interval: 50
  resume: True

  render_interval: 10

data:
  image_size: [400, 400]
  dataset_name: lego

renderer:
  type: volume
  chunk_size: 2048
  white_background: False

sampler:
  type: stratified
  n_pts_per_ray: 128

  min_depth: 2.0
  max_depth: 6.0

implicit_function:
  type: nerf

  n_harmonic_functions_xyz: 6
  n_harmonic_functions_dir: 2
  n_hidden_neurons_xyz: 256
  n_hidden_neurons_dir: 64
  density_noise_std: 0.0
  n_layers_xyz: 8
  append_xyz: [5]

Visualisation:

Part 4.3 GIF