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| """ |
| The ray marcher takes the raw output of the implicit representation and uses the volume rendering equation to produce composited colors and depths. |
| Based off of the implementation in MipNeRF (this one doesn't do any cone tracing though!) |
| """ |
|
|
| import torch |
| import torch.nn as nn |
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|
| class MipRayMarcher2(nn.Module): |
| def __init__(self, activation_factory): |
| super().__init__() |
| self.activation_factory = activation_factory |
|
|
| def run_forward(self, colors, densities, depths, rendering_options): |
| |
| deltas = depths[:, :, 1:] - depths[:, :, :-1] |
| colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2 |
| densities_mid = (densities[:, :, :-1] + densities[:, :, 1:]) / 2 |
| depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2 |
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| densities_mid = self.activation_factory(rendering_options)(densities_mid) |
|
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| density_delta = densities_mid * deltas |
|
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| alpha = 1 - torch.exp(-density_delta) |
|
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| alpha_shifted = torch.cat([torch.ones_like(alpha[:, :, :1]), 1-alpha + 1e-10], -2) |
| weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1] |
|
|
| composite_rgb = torch.sum(weights * colors_mid, -2) |
| weight_total = weights.sum(2) |
| composite_depth = torch.sum(weights * depths_mid, -2) / weight_total |
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| |
| composite_depth = torch.nan_to_num(composite_depth, float('inf')) |
| composite_depth = torch.clamp(composite_depth, torch.min(depths), torch.max(depths)) |
|
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| if rendering_options.get('white_back', False): |
| composite_rgb = composite_rgb + 1 - weight_total |
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| return composite_rgb, composite_depth, weights |
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|
| def forward(self, colors, densities, depths, rendering_options): |
| composite_rgb, composite_depth, weights = self.run_forward(colors, densities, depths, rendering_options) |
|
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| return composite_rgb, composite_depth, weights |