Create the data loader for the corresponding trainign set

Expects a two dimensional flow image of shape. Args: flow_uv (np.ndarray): Flow UV image of shape [H,W,2] clip_flow (float,

Estimate optical flow between pair of frames

near_far_from_aabb, CUDA implementation Calculate rays' intersection time (near and far) with aabb Args: rays_o: float, [

sph_from_ray, CUDA implementation get spherical coordinate on the background sphere from rays. Assume rays_o are inside the Sphere(ra

morton3D, CUDA implementation Args: coords: [N, 3], int32, in [0, 128) (for some reason there is no uint32 tensor in torch...)

morton3D_invert, CUDA implementation Args: indices: [N], int32, in [0, 128^3) Returns: coords: [N, 3], int32,

packbits, CUDA implementation Pack up the density grid into a bit field to accelerate ray marching. Args: grid: float, [C

max pooling with morton coord, CUDA implementation or maybe call it dilation... we don't support adjust kernel size. Args:

march rays to generate points (forward only) Args: rays_o/d: float, [N, 3] bound: float, scalar density_b

composite rays' rgbs, according to the ray marching formula. Args: rgbs: float, [M, 3] sigmas: float, [M,]

march rays to generate points (forward only, for inference) Args: n_alive: int, number of alive rays n_step: int, how

composite rays' rgbs, according to the ray marching formula. (for inference) Args: n_alive: int, number of alive rays

composite rays' rgbs, according to the ray marching formula. Args: rgbs: float, [M, 3] sigmas: float, [M,]

composite rays' rgbs, according to the ray marching formula. Args: rgbs: float, [M, 3] sigmas: float, [M,]

composite rays' rgbs, according to the ray marching formula. Args: rgbs: float, [M, 3] sigmas: float, [M,]