Method forward

raymarching/raymarching.py:422–444

composite rays' rgbs, according to the ray marching formula. (for inference) Args: n_alive: int, number of alive rays n_step: int, how many steps we march rays_alive: int, [n_alive], the alive rays' IDs in N (N >= n_alive) rays_t: float, [N],

(ctx, n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, normals, deltas, weights_sum, depth, image, normal, T_thresh=1e-2)

    @staticmethod
    @custom_fwd(cast_inputs=torch.float32) # need to cast sigmas & rgbs to float
    def forward(ctx, n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, normals, deltas, weights_sum, depth, image, normal, T_thresh=1e-2):
        ''' composite rays' rgbs, according to the ray marching formula. (for inference)
        Args:
            n_alive: int, number of alive rays
            n_step: int, how many steps we march
            rays_alive: int, [n_alive], the alive rays' IDs in N (N >= n_alive)
            rays_t: float, [N], the alive rays' time
            sigmas: float, [n_alive * n_step,]
            rgbs: float, [n_alive * n_step, 3]
            normals: float, [n_alive * n_step, 3]
            deltas: float, [n_alive * n_step, 2], all generated points' deltas (here we record two deltas, the first is for RGB, the second for depth).
        In-place Outputs:
            weights_sum: float, [N,], the alpha channel
            depth: float, [N,], the depth value
            image: float, [N, 3], the RGB channel (after multiplying alpha!)
            normal: float, [N, 3], the normal value
        '''
        sigmas = sigmas.float().contiguous()
        rgbs = rgbs.float().contiguous()
        normals = normals.float().contiguous()
        
        get_backend().composite_rays(n_alive, n_step, T_thresh, rays_alive, rays_t, sigmas, rgbs, normals, deltas, weights_sum, depth, image, normal)
        return tuple()


composite_rays = _composite_rays.apply