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Create shaders (#1344)
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@@ -341,37 +341,3 @@ class NormalsRenderer(nn.Module):
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if normalize:
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n = safe_normalize(n)
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return n
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class LambertianShadingRenderer(nn.Module):
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"""Calculate Lambertian shading."""
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@classmethod
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def forward(
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cls,
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rgb: TensorType["bs":..., 3],
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normals: TensorType["bs":..., 3],
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light_direction: TensorType["bs":..., 3],
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shading_weight: float = 1.0,
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detach_normals=True,
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):
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"""Calculate Lambertian shading.
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Args:
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rgb: Accumulated rgb along a ray.
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normals: Accumulated normals along a ray.
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light_direction: Direction of light source.
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shading_weight: Lambertian shading (1.0) vs. ambient lighting (0.0) ratio
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detach_normals: Detach normals from the computation graph when computing shading.
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Returns:
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Textureless Lambertian shading, Lambertian shading
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"""
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if detach_normals:
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normals = normals.detach()
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lambertian = (1 - shading_weight) + shading_weight * (normals @ light_direction).clamp(min=0)
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shaded = lambertian.unsqueeze(-1).repeat(1, 3)
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shaded_albedo = rgb * lambertian.unsqueeze(-1)
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return shaded, shaded_albedo
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@@ -0,0 +1,77 @@
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# Copyright 2022 The Nerfstudio Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Shaders for rendering."""
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from typing import Optional
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from torch import nn
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from torchtyping import TensorType
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class LambertianShader(nn.Module):
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"""Calculate Lambertian shading."""
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@classmethod
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def forward(
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cls,
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rgb: TensorType["bs":..., 3],
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normals: TensorType["bs":..., 3],
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light_direction: TensorType["bs":..., 3],
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shading_weight: float = 1.0,
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detach_normals=True,
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):
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"""Calculate Lambertian shading.
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Args:
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rgb: Accumulated rgb along a ray.
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normals: Accumulated normals along a ray.
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light_direction: Direction of light source.
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shading_weight: Lambertian shading (1.0) vs. ambient lighting (0.0) ratio
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detach_normals: Detach normals from the computation graph when computing shading.
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Returns:
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Textureless Lambertian shading, Lambertian shading
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"""
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if detach_normals:
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normals = normals.detach()
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lambertian = (1 - shading_weight) + shading_weight * (normals @ light_direction).clamp(min=0)
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shaded = lambertian.unsqueeze(-1).repeat(1, 3)
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shaded_albedo = rgb * lambertian.unsqueeze(-1)
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return shaded, shaded_albedo
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class NormalsShader(nn.Module):
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"""Calculate shading for normals."""
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@classmethod
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def forward(
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cls,
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normals: TensorType["bs":..., 3],
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weights: Optional[TensorType["bs":..., 1]] = None,
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):
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"""Applies a rainbow colormap to the normals.
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Args:
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normals: Normalized 3D vectors.
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weights: Optional weights to scale to the normal colors. (Can be used for masking)
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Returns:
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Colored normals
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"""
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normals = (normals + 1) / 2
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if weights is not None:
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normals = normals * weights
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return normals
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@@ -54,6 +54,7 @@ from nerfstudio.model_components.renderers import (
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RGBRenderer,
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)
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from nerfstudio.model_components.scene_colliders import NearFarCollider
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from nerfstudio.model_components.shaders import NormalsShader
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from nerfstudio.models.base_model import Model, ModelConfig
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from nerfstudio.utils import colormaps
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@@ -187,6 +188,9 @@ class NerfactoModel(Model):
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self.renderer_depth = DepthRenderer()
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self.renderer_normals = NormalsRenderer()
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# shaders
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self.normals_shader = NormalsShader()
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# losses
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self.rgb_loss = MSELoss()
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@@ -252,9 +256,8 @@ class NerfactoModel(Model):
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if self.config.predict_normals:
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normals = self.renderer_normals(normals=field_outputs[FieldHeadNames.NORMALS], weights=weights)
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pred_normals = self.renderer_normals(field_outputs[FieldHeadNames.PRED_NORMALS], weights=weights)
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outputs["normals"] = (normals + 1) / 2
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outputs["pred_normals"] = (pred_normals + 1) / 2
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outputs["normals"] = self.normals_shader(normals)
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outputs["pred_normals"] = self.normals_shader(pred_normals)
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# These use a lot of GPU memory, so we avoid storing them for eval.
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if self.training:
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outputs["weights_list"] = weights_list
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@@ -336,12 +339,6 @@ class NerfactoModel(Model):
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images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
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# normals to RGB for visualization. TODO: use a colormap
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if "normals" in outputs:
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images_dict["normals"] = (outputs["normals"] + 1.0) / 2.0
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if "pred_normals" in outputs:
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images_dict["pred_normals"] = (outputs["pred_normals"] + 1.0) / 2.0
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for i in range(self.config.num_proposal_iterations):
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key = f"prop_depth_{i}"
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prop_depth_i = colormaps.apply_depth_colormap(
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