83 lines
3.7 KiB
Python
83 lines
3.7 KiB
Python
# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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# property and proprietary rights in and to this material, related
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# documentation and any modifications thereto. Any use, reproduction,
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# disclosure or distribution of this material and related documentation
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# without an express license agreement from NVIDIA CORPORATION or
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# its affiliates is strictly prohibited.
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import torch
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import nvdiffrast.torch as dr
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from . import util
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from . import mesh
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######################################################################################
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# Computes the image gradient, useful for kd/ks smoothness losses
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######################################################################################
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def image_grad(buf, std=0.01):
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t, s = torch.meshgrid(torch.linspace(-1.0 + 1.0 / buf.shape[1], 1.0 - 1.0 / buf.shape[1], buf.shape[1], device="cuda"),
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torch.linspace(-1.0 + 1.0 / buf.shape[2], 1.0 - 1.0 / buf.shape[2], buf.shape[2], device="cuda"),
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) # indexing='ij')
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tc = torch.normal(mean=0, std=std, size=(buf.shape[0], buf.shape[1], buf.shape[2], 2), device="cuda") + torch.stack((s, t), dim=-1)[None, ...]
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tap = dr.texture(buf, tc, filter_mode='linear', boundary_mode='clamp')
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return torch.abs(tap[..., :-1] - buf[..., :-1]) * tap[..., -1:] * buf[..., -1:]
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######################################################################################
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# Computes the avergage edge length of a mesh.
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# Rough estimate of the tessellation of a mesh. Can be used e.g. to clamp gradients
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######################################################################################
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def avg_edge_length(v_pos, t_pos_idx):
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e_pos_idx = mesh.compute_edges(t_pos_idx)
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edge_len = util.length(v_pos[e_pos_idx[:, 0]] - v_pos[e_pos_idx[:, 1]])
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return torch.mean(edge_len)
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######################################################################################
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# Laplacian regularization using umbrella operator (Fujiwara / Desbrun).
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# https://mgarland.org/class/geom04/material/smoothing.pdf
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######################################################################################
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def laplace_regularizer_const(v_pos, t_pos_idx):
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term = torch.zeros_like(v_pos)
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norm = torch.zeros_like(v_pos[..., 0:1])
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v0 = v_pos[t_pos_idx[:, 0], :]
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v1 = v_pos[t_pos_idx[:, 1], :]
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v2 = v_pos[t_pos_idx[:, 2], :]
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term.scatter_add_(0, t_pos_idx[:, 0:1].repeat(1,3), (v1 - v0) + (v2 - v0))
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term.scatter_add_(0, t_pos_idx[:, 1:2].repeat(1,3), (v0 - v1) + (v2 - v1))
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term.scatter_add_(0, t_pos_idx[:, 2:3].repeat(1,3), (v0 - v2) + (v1 - v2))
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two = torch.ones_like(v0) * 2.0
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norm.scatter_add_(0, t_pos_idx[:, 0:1], two)
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norm.scatter_add_(0, t_pos_idx[:, 1:2], two)
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norm.scatter_add_(0, t_pos_idx[:, 2:3], two)
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term = term / torch.clamp(norm, min=1.0)
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return torch.mean(term**2)
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######################################################################################
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# Smooth vertex normals
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######################################################################################
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def normal_consistency(v_pos, t_pos_idx):
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# Compute face normals
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v0 = v_pos[t_pos_idx[:, 0], :]
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v1 = v_pos[t_pos_idx[:, 1], :]
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v2 = v_pos[t_pos_idx[:, 2], :]
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face_normals = util.safe_normalize(torch.cross(v1 - v0, v2 - v0))
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tris_per_edge = mesh.compute_edge_to_face_mapping(t_pos_idx)
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# Fetch normals for both faces sharind an edge
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n0 = face_normals[tris_per_edge[:, 0], :]
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n1 = face_normals[tris_per_edge[:, 1], :]
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# Compute error metric based on normal difference
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term = torch.clamp(util.dot(n0, n1), min=-1.0, max=1.0)
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term = (1.0 - term) * 0.5
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return torch.mean(torch.abs(term))
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