312 lines
13 KiB
Python
312 lines
13 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 numpy as np
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import nvdiffrast.torch as dr
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from . import util
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from . import renderutils as ru
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from . import light
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# ==============================================================================================
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# Helper functions
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# ==============================================================================================
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def interpolate(attr, rast, attr_idx, rast_db=None):
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return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all')
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# ==============================================================================================
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# pixel shader
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# ==============================================================================================
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def shade(
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gb_pos, gb_mask,
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gb_geometric_normal,
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gb_normal,
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gb_tangent,
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gb_texc,
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gb_texc_deriv,
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view_pos,
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lgt,
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material,
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bsdf
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):
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################################################################################
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# Texture lookups
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################################################################################
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perturbed_nrm = None
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if 'kd_ks_normal' in material:
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# Combined texture, used for MLPs because lookups are expensive
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all_tex_jitter = material['kd_ks_normal'].sample(gb_pos + torch.normal(mean=0, std=0.01, size=gb_pos.shape, device="cuda"))
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all_tex = material['kd_ks_normal'].sample(gb_pos)
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assert all_tex.shape[-1] == 9 or all_tex.shape[-1] == 10, "Combined kd_ks_normal must be 9 or 10 channels"
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kd, ks, perturbed_nrm = all_tex[..., :-6], all_tex[..., -6:-3], all_tex[..., -3:]
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# Compute albedo (kd) gradient, used for material regularizer
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kd_grad = torch.sum(torch.abs(all_tex_jitter[..., :-6] - all_tex[..., :-6]), dim=-1, keepdim=True) / 3
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else:
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kd_jitter = material['kd'].sample(gb_texc + torch.normal(mean=0, std=0.005, size=gb_texc.shape, device="cuda"), gb_texc_deriv)
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kd = material['kd'].sample(gb_texc, gb_texc_deriv)
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ks = material['ks'].sample(gb_texc, gb_texc_deriv)[..., 0:3] # skip alpha
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if 'normal' in material:
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perturbed_nrm = material['normal'].sample(gb_texc, gb_texc_deriv)
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kd_grad = torch.sum(torch.abs(kd_jitter[..., 0:3] - kd[..., 0:3]), dim=-1, keepdim=True) / 3
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# Separate kd into alpha and color, default alpha = 1
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alpha = kd[..., 3:4] if kd.shape[-1] == 4 else torch.ones_like(kd[..., 0:1])
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kd = kd[..., 0:3]
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################################################################################
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# Normal perturbation & normal bend
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################################################################################
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if 'no_perturbed_nrm' in material and material['no_perturbed_nrm']:
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perturbed_nrm = None
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gb_normal = ru.prepare_shading_normal(gb_pos, view_pos, perturbed_nrm, gb_normal, gb_tangent, gb_geometric_normal, two_sided_shading=True, opengl=True)
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################################################################################
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# Evaluate BSDF
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################################################################################
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assert 'bsdf' in material or bsdf is not None, "Material must specify a BSDF type"
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bsdf = material['bsdf'] if bsdf is None else bsdf
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if bsdf == 'pbr':
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if isinstance(lgt, light.EnvironmentLight):
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shaded_col = lgt.shade(gb_pos, gb_normal, kd, ks, view_pos, specular=True)
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else:
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assert False, "Invalid light type"
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elif bsdf == 'diffuse':
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if isinstance(lgt, light.EnvironmentLight):
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shaded_col = lgt.shade(gb_pos, gb_normal, kd, ks, view_pos, specular=False)
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else:
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assert False, "Invalid light type"
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elif bsdf == 'normal':
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shaded_col = (gb_normal + 1.0)*0.5
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elif bsdf == 'tangent':
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shaded_col = (gb_tangent + 1.0)*0.5
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elif bsdf == 'kd':
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shaded_col = kd
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elif bsdf == 'ks':
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shaded_col = ks
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else:
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assert False, "Invalid BSDF '%s'" % bsdf
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# Return multiple buffers
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buffers = {
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'shaded' : torch.cat((shaded_col, alpha), dim=-1),
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'kd_grad' : torch.cat((kd_grad, alpha), dim=-1),
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'occlusion' : torch.cat((ks[..., :1], alpha), dim=-1),
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'normal' : torch.cat(((gb_normal + 1.0) * 0.5, gb_mask), dim=-1),
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}
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return buffers
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# ==============================================================================================
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# Render a depth slice of the mesh (scene), some limitations:
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# - Single mesh
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# - Single light
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# - Single material
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# ==============================================================================================
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def render_layer(
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rast,
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rast_deriv,
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mesh,
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view_pos,
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lgt,
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resolution,
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spp,
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msaa,
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bsdf
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):
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full_res = [resolution[0]*spp, resolution[1]*spp]
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################################################################################
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# Rasterize
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################################################################################
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# Scale down to shading resolution when MSAA is enabled, otherwise shade at full resolution
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if spp > 1 and msaa:
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rast_out_s = util.scale_img_nhwc(rast, resolution, mag='nearest', min='nearest')
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rast_out_deriv_s = util.scale_img_nhwc(rast_deriv, resolution, mag='nearest', min='nearest') * spp
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else:
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rast_out_s = rast
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rast_out_deriv_s = rast_deriv
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################################################################################
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# Interpolate attributes
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################################################################################
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# Interpolate world space position
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gb_pos, _ = interpolate(mesh.v_pos[None, ...], rast_out_s, mesh.t_pos_idx.int())
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gb_mask, _ = interpolate(torch.ones_like(mesh.v_pos[None, ..., :1]), rast_out_s, mesh.t_pos_idx.int())
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# Compute geometric normals. We need those because of bent normals trick (for bump mapping)
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v0 = mesh.v_pos[mesh.t_pos_idx[:, 0], :]
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v1 = mesh.v_pos[mesh.t_pos_idx[:, 1], :]
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v2 = mesh.v_pos[mesh.t_pos_idx[:, 2], :]
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face_normals = util.safe_normalize(torch.cross(v1 - v0, v2 - v0))
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face_normal_indices = (torch.arange(0, face_normals.shape[0], dtype=torch.int64, device='cuda')[:, None]).repeat(1, 3)
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gb_geometric_normal, _ = interpolate(face_normals[None, ...], rast_out_s, face_normal_indices.int())
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# Compute tangent space
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assert mesh.v_nrm is not None and mesh.v_tng is not None
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gb_normal, _ = interpolate(mesh.v_nrm[None, ...], rast_out_s, mesh.t_nrm_idx.int())
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gb_tangent, _ = interpolate(mesh.v_tng[None, ...], rast_out_s, mesh.t_tng_idx.int()) # Interpolate tangents
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# Texture coordinate
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assert mesh.v_tex is not None
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gb_texc, gb_texc_deriv = interpolate(mesh.v_tex[None, ...], rast_out_s, mesh.t_tex_idx.int(), rast_db=rast_out_deriv_s)
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################################################################################
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# Shade
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################################################################################
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buffers = shade(gb_pos, gb_mask, gb_geometric_normal, gb_normal, gb_tangent, gb_texc, gb_texc_deriv, view_pos, lgt, mesh.material, bsdf)
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################################################################################
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# Prepare output
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################################################################################
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# Scale back up to visibility resolution if using MSAA
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if spp > 1 and msaa:
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for key in buffers.keys():
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buffers[key] = util.scale_img_nhwc(buffers[key], full_res, mag='nearest', min='nearest')
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# Return buffers
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return buffers
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# ==============================================================================================
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# Render a depth peeled mesh (scene), some limitations:
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# - Single mesh
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# - Single light
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# - Single material
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# ==============================================================================================
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def render_mesh(
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ctx,
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mesh,
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mtx_in, # mvp, [B, 4, 4]
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view_pos, # cam pos, [B, 3]
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lgt,
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resolution, # [2] (check the custom collate in dataset/dataset.py)
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spp = 1,
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num_layers = 1, # always 1
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msaa = False,
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background = None,
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bsdf = None
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):
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def prepare_input_vector(x):
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x = torch.tensor(x, dtype=torch.float32, device='cuda') if not torch.is_tensor(x) else x
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return x[:, None, None, :] if len(x.shape) == 2 else x
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def composite_buffer(key, layers, background, antialias):
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accum = background
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for buffers, rast in reversed(layers):
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alpha = (rast[..., -1:] > 0).float() * buffers[key][..., -1:]
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accum = torch.lerp(accum, torch.cat((buffers[key][..., :-1], torch.ones_like(buffers[key][..., -1:])), dim=-1), alpha)
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if antialias:
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accum = dr.antialias(accum.contiguous(), rast, v_pos_clip, mesh.t_pos_idx.int())
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return accum
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assert mesh.t_pos_idx.shape[0] > 0, "Got empty training triangle mesh (unrecoverable discontinuity)"
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assert background is None or (background.shape[1] == resolution[0] and background.shape[2] == resolution[1])
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full_res = [resolution[0]*spp, resolution[1]*spp]
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# Convert numpy arrays to torch tensors
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mtx_in = torch.tensor(mtx_in, dtype=torch.float32, device='cuda') if not torch.is_tensor(mtx_in) else mtx_in
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view_pos = prepare_input_vector(view_pos) # [B, 1, 1, 3]
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# clip space transform
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v_pos_clip = ru.xfm_points(mesh.v_pos[None, ...], mtx_in) # just the mvp transform, [1, N, 3]
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# Render all layers front-to-back
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layers = []
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with dr.DepthPeeler(ctx, v_pos_clip, mesh.t_pos_idx.int(), full_res) as peeler:
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for _ in range(num_layers):
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rast, db = peeler.rasterize_next_layer()
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layers += [(render_layer(rast, db, mesh, view_pos, lgt, resolution, spp, msaa, bsdf), rast)]
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# Setup background
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if background is not None:
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if spp > 1:
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background = util.scale_img_nhwc(background, full_res, mag='nearest', min='nearest')
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background = torch.cat((background, torch.zeros_like(background[..., 0:1])), dim=-1)
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else:
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background = torch.zeros(1, full_res[0], full_res[1], 4, dtype=torch.float32, device='cuda')
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# Composite layers front-to-back
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out_buffers = {}
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for key in layers[0][0].keys():
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if key == 'shaded':
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accum = composite_buffer(key, layers, background, True)
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elif key == 'normal':
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accum = composite_buffer(key, layers, torch.zeros_like(layers[0][0][key]), True)
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else:
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accum = composite_buffer(key, layers, torch.zeros_like(layers[0][0][key]), False)
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# Downscale to framebuffer resolution. Use avg pooling
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out_buffers[key] = util.avg_pool_nhwc(accum, spp) if spp > 1 else accum
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return out_buffers
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# ==============================================================================================
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# Render UVs
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# ==============================================================================================
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def render_uv(ctx, mesh, resolution, mlp_texture):
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# clip space transform
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uv_clip = mesh.v_tex[None, ...]*2.0 - 1.0
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# pad to four component coordinate
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uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[...,0:1]), torch.ones_like(uv_clip[...,0:1])), dim = -1)
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# rasterize
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rast, _ = dr.rasterize(ctx, uv_clip4, mesh.t_tex_idx.int(), resolution)
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# Interpolate world space position
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gb_pos, _ = interpolate(mesh.v_pos[None, ...], rast, mesh.t_pos_idx.int())
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# Sample out textures from MLP
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all_tex = mlp_texture.sample(gb_pos)
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assert all_tex.shape[-1] == 9 or all_tex.shape[-1] == 10, "Combined kd_ks_normal must be 9 or 10 channels"
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mask = (rast[..., -1:] > 0).float()
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kd = all_tex[..., :-6]
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ks = all_tex[..., -6:-3]
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perturbed_nrm = util.safe_normalize(all_tex[..., -3:])
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# antialiasing
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from sklearn.neighbors import NearestNeighbors
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from scipy.ndimage import binary_dilation, binary_erosion
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mask_np = mask.cpu().numpy() > 0
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inpaint_region = binary_dilation(mask_np, iterations=3)
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inpaint_region[mask_np] = 0
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search_region = mask_np.copy()
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not_search_region = binary_erosion(search_region, iterations=2)
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search_region[not_search_region] = 0
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search_coords = np.stack(np.nonzero(search_region), axis=-1)
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inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)
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knn = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(search_coords)
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_, indices = knn.kneighbors(inpaint_coords)
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inpaint_coords = torch.from_numpy(inpaint_coords).long().to(kd.device)
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target_coords = torch.from_numpy(search_coords[indices[:, 0]]).long().to(kd.device)
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kd[tuple(inpaint_coords.T)] = kd[tuple(target_coords.T)]
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ks[tuple(inpaint_coords.T)] = ks[tuple(target_coords.T)]
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perturbed_nrm[tuple(inpaint_coords.T)] = perturbed_nrm[tuple(target_coords.T)]
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return mask, kd, ks, perturbed_nrm
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