Pros of NeRF

• Original implementation by the authors of the NeRF paper
• Provides a reference implementation for understanding the core NeRF algorithm
• Includes additional features like depth supervision and view dependence

Cons of NeRF

• Written in TensorFlow, which may be less popular among some researchers
• Less optimized for speed compared to more recent implementations
• Lacks some modern features and improvements found in newer NeRF variants

Code Comparison

NeRF (TensorFlow):

def create_nerf(args):
    embed_fn, input_ch = get_embedder(args.multires, args.i_embed)
    embeddirs_fn, input_ch_views = get_embedder(args.multires_views, args.i_embed)
    output_ch = 5 if args.N_importance > 0 else 4
    skips = [4]
    model = init_nerf_model(D=args.netdepth, W=args.netwidth,
                            input_ch=input_ch, output_ch=output_ch, skips=skips,
                            input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs)

nerf_pl (PyTorch):

class NeRF(nn.Module):
    def __init__(self,
                 D=8, W=256,
                 in_channels_xyz=63, in_channels_dir=27,
                 skips=[4]):
        super().__init__()
        self.D = D
        self.W = W
        self.in_channels_xyz = in_channels_xyz
        self.in_channels_dir = in_channels_dir
        self.skips = skips

Pros of nerf-pytorch

• More straightforward implementation, closely following the original NeRF paper
• Easier to understand for those new to NeRF concepts
• Includes a colab notebook for quick experimentation

Cons of nerf-pytorch

• Less optimized for performance compared to nerf_pl
• Fewer features and customization options
• Limited support for advanced NeRF variants

Code Comparison

nerf-pytorch:

def get_rays(H, W, K, c2w):
    i, j = torch.meshgrid(torch.linspace(0, W-1, W), torch.linspace(0, H-1, H))
    i = i.t()
    j = j.t()
    dirs = torch.stack([(i-K[0][2])/K[0][0], -(j-K[1][2])/K[1][1], -torch.ones_like(i)], -1)
    rays_d = torch.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)
    rays_o = c2w[:3,-1].expand(rays_d.shape)
    return rays_o, rays_d

nerf_pl:

def get_rays(directions, c2w):
    rays_d = directions @ c2w[:3, :3].T
    rays_o = c2w[:3, 3].expand(rays_d.shape)
    return rays_o, rays_d

The nerf_pl implementation is more concise and optimized, while nerf-pytorch provides a more detailed breakdown of the ray generation process.

Pros of instant-ngp

• Significantly faster rendering and training times
• Supports real-time rendering and interactive visualization
• Utilizes GPU acceleration for improved performance

Cons of instant-ngp

• More complex implementation, potentially harder to understand and modify
• Requires specific hardware (NVIDIA GPU) for optimal performance
• Less flexibility in terms of customization compared to nerf_pl

Code Comparison

instant-ngp:

__global__ void render_kernel(
    const uint32_t n_elements,
    const uint32_t n_rays,
    BoundingBox aabb,
    const uint32_t max_samples,
    const float cone_angle_constant,
    // ... (additional parameters)
) {
    // Complex CUDA kernel implementation
}

nerf_pl:

def render_rays(models, embeddings,
                rays, N_samples, use_disp,
                perturb, noise_std, N_importance,
                chunk, white_back, test_time=False,
                **kwargs):
    # Python implementation of ray rendering

The code comparison shows that instant-ngp uses CUDA for GPU acceleration, while nerf_pl is implemented in Python, which is more accessible but potentially slower.

Pros of pytorch3d

• Comprehensive 3D deep learning library with a wide range of functionalities
• Backed by Facebook Research, ensuring regular updates and support
• Extensive documentation and tutorials available

Cons of pytorch3d

• Steeper learning curve due to its broader scope
• May be overkill for projects focused solely on NeRF implementations

Code Comparison

pytorch3d example:

import torch
from pytorch3d.structures import Meshes
from pytorch3d.renderer import Textures

verts = torch.randn(4, 3)
faces = torch.tensor([[0, 1, 2], [1, 2, 3]])
mesh = Meshes(verts=[verts], faces=[faces])

nerf_pl example:

import torch
from models.nerf import NeRF

model = NeRF()
rays_o = torch.randn(1, 3)
rays_d = torch.randn(1, 3)
output = model(rays_o, rays_d)

pytorch3d offers a more comprehensive set of tools for 3D deep learning, while nerf_pl provides a focused implementation of NeRF. The choice between them depends on the specific requirements of your project and your familiarity with 3D computer vision concepts.

Pros of google-research

• Extensive collection of research projects covering various AI and ML domains
• Official repository from Google, ensuring high-quality and well-maintained code
• Regular updates and contributions from Google researchers

Cons of google-research

• Large repository size may be overwhelming for users seeking specific implementations
• Less focused on NeRF specifically, requiring more effort to find relevant code
• May have more complex dependencies due to the diverse range of projects

Code Comparison

google-research (NeRF-related code snippet):

def render_rays(ray_batch,
                network_fn,
                network_query_fn,
                N_samples,
                retraw=False,
                lindisp=False,
                perturb=0.,
                N_importance=0,
                network_fine=None,
                white_bkgd=False,
                raw_noise_std=0.,
                verbose=False):
    # ... (implementation details)

nerf_pl:

def render_rays(models,
                embeddings,
                rays,
                N_samples=64,
                use_disp=False,
                perturb=0,
                noise_std=1,
                N_importance=0,
                chunk=1024*32,
                white_background=False,
                test_time=False,
                **kwargs):
    # ... (implementation details)

Both repositories provide implementations for NeRF, but google-research offers a broader scope of research projects, while nerf_pl focuses specifically on NeRF implementation using PyTorch Lightning.

Pros of nerfstudio

• More comprehensive and feature-rich, offering a wider range of NeRF methods and tools
• Better documentation and user-friendly interface, making it easier for beginners to get started
• Active development and community support, with frequent updates and improvements

Cons of nerfstudio

• Higher complexity and steeper learning curve for customization
• Potentially slower training and inference times due to additional features and abstractions

Code Comparison

nerf_pl:

class NeRF(nn.Module):
    def __init__(self,
                 D=8, W=256,
                 in_channels_xyz=3, in_channels_dir=3,
                 skips=[4]):
        super().__init__()
        self.D = D
        self.W = W
        self.skips = skips

nerfstudio:

class NeRFModel(Model):
    config: ModelConfig

    def populate_modules(self):
        """Set the fields and modules."""
        super().populate_modules()

        self.field = TCNNNerfactoField(
            self.config.scene_bounds.aabb,
            num_layers=self.config.num_layers,
            hidden_dim=self.config.hidden_dim,
            geo_feat_dim=self.config.geo_feat_dim,
            num_layers_color=self.config.num_layers_color,
            hidden_dim_color=self.config.hidden_dim_color,
            use_appearance_embedding=self.config.use_appearance_embedding,
        )