Create shaders (#1344)

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Matthew Tancik authored and GitHub committed 2023-02-03 11:21:54 -08:00
1 parent a0976d1ff8
commit 59d7e99c80
3 files changed
+83 -43

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-34
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@@ -341,37 +341,3 @@ class NormalsRenderer(nn.Module):
if normalize:
n = safe_normalize(n)
return n
class LambertianShadingRenderer(nn.Module):
"""Calculate Lambertian shading."""
@classmethod
def forward(
cls,
rgb: TensorType["bs":..., 3],
normals: TensorType["bs":..., 3],
light_direction: TensorType["bs":..., 3],
shading_weight: float = 1.0,
detach_normals=True,
):
"""Calculate Lambertian shading.
Args:
rgb: Accumulated rgb along a ray.
normals: Accumulated normals along a ray.
light_direction: Direction of light source.
shading_weight: Lambertian shading (1.0) vs. ambient lighting (0.0) ratio
detach_normals: Detach normals from the computation graph when computing shading.
Returns:
Textureless Lambertian shading, Lambertian shading
"""
if detach_normals:
normals = normals.detach()
lambertian = (1 - shading_weight) + shading_weight * (normals @ light_direction).clamp(min=0)
shaded = lambertian.unsqueeze(-1).repeat(1, 3)
shaded_albedo = rgb * lambertian.unsqueeze(-1)
return shaded, shaded_albedo
+77
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@@ -0,0 +1,77 @@
# Copyright 2022 The Nerfstudio Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Shaders for rendering."""
from typing import Optional
from torch import nn
from torchtyping import TensorType
class LambertianShader(nn.Module):
"""Calculate Lambertian shading."""
@classmethod
def forward(
cls,
rgb: TensorType["bs":..., 3],
normals: TensorType["bs":..., 3],
light_direction: TensorType["bs":..., 3],
shading_weight: float = 1.0,
detach_normals=True,
):
"""Calculate Lambertian shading.
Args:
rgb: Accumulated rgb along a ray.
normals: Accumulated normals along a ray.
light_direction: Direction of light source.
shading_weight: Lambertian shading (1.0) vs. ambient lighting (0.0) ratio
detach_normals: Detach normals from the computation graph when computing shading.
Returns:
Textureless Lambertian shading, Lambertian shading
"""
if detach_normals:
normals = normals.detach()
lambertian = (1 - shading_weight) + shading_weight * (normals @ light_direction).clamp(min=0)
shaded = lambertian.unsqueeze(-1).repeat(1, 3)
shaded_albedo = rgb * lambertian.unsqueeze(-1)
return shaded, shaded_albedo
class NormalsShader(nn.Module):
"""Calculate shading for normals."""
@classmethod
def forward(
cls,
normals: TensorType["bs":..., 3],
weights: Optional[TensorType["bs":..., 1]] = None,
):
"""Applies a rainbow colormap to the normals.
Args:
normals: Normalized 3D vectors.
weights: Optional weights to scale to the normal colors. (Can be used for masking)
Returns:
Colored normals
"""
normals = (normals + 1) / 2
if weights is not None:
normals = normals * weights
return normals
+6 -9
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@@ -54,6 +54,7 @@ from nerfstudio.model_components.renderers import (
RGBRenderer,
)
from nerfstudio.model_components.scene_colliders import NearFarCollider
from nerfstudio.model_components.shaders import NormalsShader
from nerfstudio.models.base_model import Model, ModelConfig
from nerfstudio.utils import colormaps
@@ -187,6 +188,9 @@ class NerfactoModel(Model):
self.renderer_depth = DepthRenderer()
self.renderer_normals = NormalsRenderer()
# shaders
self.normals_shader = NormalsShader()
# losses
self.rgb_loss = MSELoss()
@@ -252,9 +256,8 @@ class NerfactoModel(Model):
if self.config.predict_normals:
normals = self.renderer_normals(normals=field_outputs[FieldHeadNames.NORMALS], weights=weights)
pred_normals = self.renderer_normals(field_outputs[FieldHeadNames.PRED_NORMALS], weights=weights)
outputs["normals"] = (normals + 1) / 2
outputs["pred_normals"] = (pred_normals + 1) / 2
outputs["normals"] = self.normals_shader(normals)
outputs["pred_normals"] = self.normals_shader(pred_normals)
# These use a lot of GPU memory, so we avoid storing them for eval.
if self.training:
outputs["weights_list"] = weights_list
@@ -336,12 +339,6 @@ class NerfactoModel(Model):
images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
# normals to RGB for visualization. TODO: use a colormap
if "normals" in outputs:
images_dict["normals"] = (outputs["normals"] + 1.0) / 2.0
if "pred_normals" in outputs:
images_dict["pred_normals"] = (outputs["pred_normals"] + 1.0) / 2.0
for i in range(self.config.num_proposal_iterations):
key = f"prop_depth_{i}"
prop_depth_i = colormaps.apply_depth_colormap(