Pros of nerf_pl

• Utilizes PyTorch Lightning, offering better organization and scalability
• Includes more advanced features like depth supervision and dynamic batch size
• Provides comprehensive documentation and examples for easier usage

Cons of nerf_pl

• May have a steeper learning curve due to PyTorch Lightning integration
• Potentially higher computational requirements for some advanced features
• Less suitable for users seeking a minimal, vanilla NeRF implementation

Code Comparison

nerf-pytorch:

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

nerf_pl:

rays = get_rays(directions, c2w)
results = self(rays, img_idx, **kwargs)
rgb, depth, opacity = results[:3]
extras = results[3:]

The nerf_pl implementation leverages PyTorch Lightning's modular structure, while nerf-pytorch uses a more traditional approach. nerf_pl's code is generally more concise and organized due to its use of PyTorch Lightning's conventions.

Pros of nerf

• Original implementation by the NeRF authors, ensuring high fidelity to the paper
• Includes additional features like depth rendering and view interpolation
• Comprehensive documentation and explanations in the README

Cons of nerf

• Written in TensorFlow, which may be less familiar to some researchers
• Potentially slower training and inference compared to PyTorch implementations
• Less active maintenance and updates compared to community-driven implementations

Code Comparison

nerf (TensorFlow):

def create_nerf(args):
    """Instantiate NeRF's MLP model."""
    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]

nerf-pytorch (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

Pros of instant-ngp

• Significantly faster training and rendering times
• Supports real-time rendering and interactive visualization
• Implements advanced techniques like hash encoding and multi-resolution hierarchies

Cons of instant-ngp

• More complex implementation, potentially harder to understand and modify
• Requires CUDA-capable GPU for optimal performance
• Less flexible in terms of customization compared to nerf-pytorch

Code Comparison

nerf-pytorch:

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)

instant-ngp:

__global__ void training_kernel(
    const uint32_t n_rays,
    BoundingBox aabb,
    const uint32_t n_rays_total,
    default_rng_t rng,
    const uint32_t max_samples_compacted,
    const uint32_t padded_output_width,
    const uint32_t n_training_images,
    const TrainingImageMetadata* __restrict__ metadata,
    // ... (more parameters)
) {
    // ... (implementation details)
}

The code comparison shows that instant-ngp uses CUDA kernels for performance optimization, while nerf-pytorch uses standard Python functions. This reflects the focus on speed and real-time rendering in instant-ngp, compared to the more straightforward implementation in nerf-pytorch.

Pros of pytorch3d

• Broader scope: Offers a comprehensive suite of 3D vision tools beyond NeRF
• Active development: Regularly updated with new features and improvements
• Extensive documentation: Well-documented with tutorials and examples

Cons of pytorch3d

• Steeper learning curve: More complex due to its broader feature set
• Heavier resource requirements: May need more computational power for some operations

Code Comparison

nerf-pytorch:

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

pytorch3d:

cameras = PerspectiveCameras(device=device, R=R, T=T)
renderer = MeshRenderer(
    rasterizer=MeshRasterizer(cameras=cameras),
    shader=SoftPhongShader(device=device, cameras=cameras)
)
images = renderer(meshes, cameras=cameras, lights=lights)

Both repositories provide implementations for 3D rendering, but pytorch3d offers a more generalized approach with its camera and renderer classes, while nerf-pytorch focuses specifically on Neural Radiance Fields.

Pros of google-research

• Broader scope, covering various research projects and topics
• Regularly updated with new research and implementations
• Backed by Google, ensuring high-quality and cutting-edge research

Cons of google-research

• Less focused on NeRF specifically, making it harder to find relevant code
• May be more complex to navigate due to the wide range of projects
• Potentially steeper learning curve for beginners in specific areas

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

google-research (NeRF-related code):

def get_rays(height, width, focal, pose):
    """Compute the ray origins and directions for all pixels."""
    x, y = np.meshgrid(
        np.arange(width, dtype=np.float32),
        np.arange(height, dtype=np.float32),
        indexing='xy')
    directions = np.stack(
        [(x - width * 0.5) / focal,
         -(y - height * 0.5) / focal,
         -np.ones_like(x)], axis=-1)
    ray_directions = np.sum(directions[..., np.newaxis, :] * pose[:3, :3], axis=-1)
    ray_origins = np.broadcast_to(pose[:3, -1], ray_directions.shape)
    return ray_origins, ray_directions

Pros of svox2

• Faster rendering and training times due to its voxel-based approach
• Better handling of large-scale scenes with improved memory efficiency
• Supports both bounded and unbounded scenes

Cons of svox2

• More complex implementation compared to the simpler NeRF approach
• May require more parameter tuning for optimal results
• Potential loss of fine details in some cases due to voxelization

Code Comparison

svox2:

render_opts = RenderOptions(
    step_size=step_size,
    sigma_thresh=1e-8,
    stop_thresh=1e-7
)
rgb, depth, _ = model.volume_render(rays, render_opts)

nerf-pytorch:

raw = network_query_fn(pts, viewdirs, network_fn)
rgb, disp, acc, weights, depth = raw2outputs(raw, z_vals, rays_d, raw_noise_std, white_bkgd)

Both repositories implement neural radiance fields, but svox2 uses a voxel-based approach for improved efficiency, while nerf-pytorch follows the original NeRF implementation more closely. svox2 offers faster rendering and better scalability, but may require more setup and tuning. nerf-pytorch provides a simpler implementation that closely adheres to the original NeRF paper, making it easier to understand and modify.