Pros of nerf_pl

• Implemented in PyTorch Lightning, offering better organization and scalability
• Includes additional features like depth supervision and dynamic batch size
• Provides a more user-friendly interface with command-line arguments

Cons of nerf_pl

• May have slightly slower training speed compared to the original implementation
• Might require more GPU memory due to PyTorch Lightning overhead
• Less established and potentially less stable than the original NeRF repository

Code Comparison

nerf:

def batchify(fn, chunk=1024*32):
    if chunk is None:
        return fn
    def ret(inputs):
        return torch.cat([fn(inputs[i:i+chunk]) for i in range(0, inputs.shape[0], chunk)], 0)
    return ret

nerf_pl:

def batchify(func, chunk):
    if chunk is None:
        return func
    def ret(inputs):
        return torch.cat([func(inputs[i:i+chunk]) for i in range(0, inputs.shape[0], chunk)], 0)
    return ret

The code comparison shows that both implementations use similar batchifying techniques, with minor differences in variable naming and formatting.

Pros of nerf-pytorch

• Implemented in PyTorch, offering better compatibility with PyTorch-based projects
• Includes additional features like custom ray sampling and depth-guided sampling
• More actively maintained with recent updates and contributions

Cons of nerf-pytorch

• May have slightly slower inference time compared to the original TensorFlow implementation
• Lacks some of the advanced features present in the original NeRF repository

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)
    return model

nerf-pytorch:

class NeRF(nn.Module):
    def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False):
        super(NeRF, self).__init__()
        self.D = D
        self.W = W
        self.input_ch = input_ch
        self.input_ch_views = input_ch_views
        self.skips = skips
        self.use_viewdirs = use_viewdirs
        
        self.pts_linears = nn.ModuleList(
            [nn.Linear(input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + input_ch, W) for i in range(D-1)])

Pros of instant-ngp

• Significantly faster training and rendering times
• Supports a wider range of 3D representations (NeRF, signed distance functions, volumetric grids)
• Includes real-time viewer for interactive exploration of trained models

Cons of instant-ngp

• More complex implementation, potentially harder to understand and modify
• Requires CUDA-capable GPU for optimal performance
• Less focus on original NeRF methodology, may not be suitable for direct NeRF comparisons

Code Comparison

instant-ngp:

// Multi-resolution hash encoding
uint32_t hash = hash_function(x, y, z, level);
float* encoded_value = hash_lookup(hash);
network_input = concat(encoded_value, direction);

nerf:

# Positional encoding
encoded_x = [sin(2^i * pi * x) for i in range(L)]
encoded_y = [sin(2^i * pi * y) for i in range(L)]
encoded_z = [sin(2^i * pi * z) for i in range(L)]
network_input = concat(encoded_x, encoded_y, encoded_z, direction)

The code snippets illustrate the different encoding approaches used by each project. instant-ngp uses a multi-resolution hash encoding, while nerf employs positional encoding for input coordinates.

Pros of PyTorch3D

• Broader scope: Offers a comprehensive suite of tools for 3D computer vision, including rendering, mesh operations, and point cloud processing
• Better integration: Seamlessly integrates with PyTorch ecosystem, making it easier to use with existing deep learning workflows
• Active development: Regularly updated with new features and improvements by Facebook Research team

Cons of PyTorch3D

• Steeper learning curve: More complex API due to its broader feature set, potentially requiring more time to master
• Higher computational requirements: May require more powerful hardware for some operations compared to NeRF's focused implementation

Code Comparison

PyTorch3D example (rendering a mesh):

renderer = MeshRenderer(
    rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
    shader=SoftPhongShader(device=device, cameras=cameras)
)
images = renderer(meshes, lights=lights, materials=materials)

NeRF example (rendering a scene):

rays_o, rays_d = get_rays(H, W, K, c2w)
rgb, disp, acc, extras = render(H, W, K, chunk=args.chunk, rays=rays,
                                verbose=i < 10, retraw=True,
                                **render_kwargs)

Pros of google-research

• Broader scope, covering various research areas beyond just NeRF
• More active development with frequent updates and contributions
• Extensive documentation and examples for multiple projects

Cons of google-research

• Less focused on NeRF specifically, making it harder to find relevant code
• Potentially more complex to navigate due to the large number of projects
• May require more setup and dependencies for individual projects

Code Comparison

NeRF (simplified render_rays function):

def render_rays(ray_batch, network_fn, network_query_fn, N_samples, retraw=False, lindisp=False, perturb=0.):
    # ... (implementation details)
    return rgb_map, disp_map, acc_map, weights, depth_map

google-research (example from nerf project):

def render_image(render_fn, rays, rng, chunk=8192, verbose=False):
    height, width = rays.shape[:2]
    num_rays = height * width
    rays = rays.reshape((num_rays, -1))
    results = []
    # ... (implementation details)

Both repositories implement NeRF-related functionality, but google-research offers a more generalized approach within a larger research framework.

Pros of svox2

• Faster rendering and training times compared to NeRF
• Supports dynamic scenes and deformable objects
• More memory-efficient representation of 3D scenes

Cons of svox2

• May produce slightly lower quality results in some cases
• Requires more complex implementation and setup
• Less widely adopted and tested compared to NeRF

Code Comparison

NeRF (Python):

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)

svox2 (C++):

void RenderRays(
    const Rays& rays,
    const Grid& grid,
    const RenderOptions& options,
    RenderBuffer& out) {
    // ... (implementation details)
}

The code snippets show that NeRF uses Python for its implementation, while svox2 utilizes C++ for core rendering functions, potentially contributing to its speed advantages. svox2's rendering function appears more streamlined, reflecting its optimized approach to volume rendering.