# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual # property and proprietary rights in and to this material, related # documentation and any modifications thereto. Any use, reproduction, # disclosure or distribution of this material and related documentation # without an express license agreement from NVIDIA CORPORATION or # its affiliates is strictly prohibited. import torch import nvdiffrast.torch as dr from . import util from . import renderutils as ru from . import light # ============================================================================================== # Helper functions # ============================================================================================== def interpolate(attr, rast, attr_idx, rast_db=None): return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all') # ============================================================================================== # pixel shader # ============================================================================================== def shade( gb_pos, gb_geometric_normal, gb_normal, gb_tangent, gb_texc, gb_texc_deriv, view_pos, lgt, material, bsdf, xfm_lgt=None ): ################################################################################ # Texture lookups ################################################################################ perturbed_nrm = None alpha_mtl = None if 'kd_ks_normal' in material: # Combined texture, used for MLPs because lookups are expensive all_tex_jitter = material['kd_ks_normal'].sample(gb_pos + torch.normal(mean=0, std=0.01, size=gb_pos.shape, device="cuda")) all_tex = material['kd_ks_normal'].sample(gb_pos) assert all_tex.shape[-1] == 9 or all_tex.shape[-1] == 10, "Combined kd_ks_normal must be 9 or 10 channels" kd, ks, perturbed_nrm = all_tex[..., :-6], all_tex[..., -6:-3], all_tex[..., -3:] # Compute albedo (kd) gradient, used for material regularizer kd_grad = torch.sum(torch.abs(all_tex_jitter[..., :-6] - all_tex[..., :-6]), dim=-1, keepdim=True) / 3 else: try: kd_jitter = material['kd'].sample(gb_texc + torch.normal(mean=0, std=0.005, size=gb_texc.shape, device="cuda"), gb_texc_deriv) if 'alpha' in material: raise NotImplementedError try: alpha_mtl = material['alpha'].sample(gb_texc, gb_texc_deriv) except: alpha_mtl = material['alpha'].sample(gb_pos + torch.normal(mean=0, std=0.01, size=gb_pos.shape, device="cuda")) kd = material['kd'].sample(gb_texc, gb_texc_deriv) ks = material['ks'].sample(gb_texc, gb_texc_deriv)[..., 0:3] # skip alpha kd_grad = torch.sum(torch.abs(kd_jitter[..., 0:3] - kd[..., 0:3]), dim=-1, keepdim=True) / 3 except: kd_jitter = kd = material['kd'].data[0].expand(*gb_pos.size()) ks = material['ks'].data[0].expand(*gb_pos.size())[..., 0:3] # skip alpha kd_grad = torch.sum(torch.abs(kd_jitter[..., 0:3] - kd[..., 0:3]), dim=-1, keepdim=True) / 3 # Separate kd into alpha and color, default alpha = 1 alpha = kd[..., 3:4] if kd.shape[-1] == 4 else torch.ones_like(kd[..., 0:1]) if alpha_mtl is not None: alpha = alpha_mtl kd = kd[..., 0:3] ################################################################################ # Normal perturbation & normal bend ################################################################################ if 'no_perturbed_nrm' in material and material['no_perturbed_nrm']: perturbed_nrm = None use_python = (gb_tangent is None) 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, use_python=use_python) gb_geo_normal_corrected = ru.prepare_shading_normal(gb_pos, view_pos, None, gb_geometric_normal, gb_tangent, gb_geometric_normal, two_sided_shading=True, opengl=True, use_python=use_python) ################################################################################ # Evaluate BSDF ################################################################################ assert 'bsdf' in material or bsdf is not None, "Material must specify a BSDF type" bsdf = material['bsdf'] if bsdf is None else bsdf if bsdf == 'pbr': # do not use pbr raise NotImplementedError if isinstance(lgt, light.EnvironmentLight): shaded_col = lgt.shade(gb_pos, gb_normal, kd, ks, view_pos, specular=True) else: assert False, "Invalid light type" elif bsdf == 'diffuse': if isinstance(lgt, light.EnvironmentLight): shaded_col = lgt.shade(gb_pos, gb_geo_normal_corrected, kd, ks, view_pos, specular=False, xfm_lgt=xfm_lgt) else: assert False, "Invalid light type" elif bsdf == 'normal': shaded_col = (gb_normal + 1.0)*0.5 elif bsdf == 'tangent': shaded_col = (gb_tangent + 1.0)*0.5 elif bsdf == 'kd': shaded_col = kd elif bsdf == 'ks': shaded_col = ks else: assert False, "Invalid BSDF '%s'" % bsdf nan_mask = torch.isnan(shaded_col) if nan_mask.any(): raise if alpha is not None: nan_mask = torch.isnan(alpha) if nan_mask.any(): raise # Return multiple buffers buffers = { 'shaded' : torch.cat((shaded_col, alpha), dim=-1), 'kd_grad' : torch.cat((kd_grad, alpha), dim=-1), 'occlusion' : torch.cat((ks[..., :1], alpha), dim=-1), 'normal' : torch.cat((gb_normal, alpha), dim=-1), 'depth' : torch.cat(((gb_pos - view_pos).pow(2).sum(dim=-1, keepdim=True).sqrt(), alpha), dim=-1), 'pos' : torch.cat((gb_pos, alpha), dim=-1), 'geo_normal': torch.cat((gb_geo_normal_corrected, alpha), dim=-1), 'geo_viewdir': torch.cat((view_pos - gb_pos, alpha), dim=-1), 'alpha' : alpha } return buffers # ============================================================================================== # Render a depth slice of the mesh (scene), some limitations: # - Single mesh # - Single light # - Single material # ============================================================================================== def render_layer( rast, rast_deriv, mesh, view_pos, lgt, resolution, spp, msaa, bsdf, xfm_lgt = None, flat_shading = False ): full_res = [resolution[0]*spp, resolution[1]*spp] ################################################################################ # Rasterize ################################################################################ # Scale down to shading resolution when MSAA is enabled, otherwise shade at full resolution if spp > 1 and msaa: rast_out_s = util.scale_img_nhwc(rast, resolution, mag='nearest', min='nearest') else: rast_out_s = rast ################################################################################ # Interpolate attributes ################################################################################ # Interpolate world space position gb_pos, _ = interpolate(mesh.v_pos[None, ...], rast_out_s, mesh.t_pos_idx.int()) # Compute geometric normals. We need those because of bent normals trick (for bump mapping) v0 = mesh.v_pos[mesh.t_pos_idx[:, 0], :] v1 = mesh.v_pos[mesh.t_pos_idx[:, 1], :] v2 = mesh.v_pos[mesh.t_pos_idx[:, 2], :] face_normals = util.safe_normalize(torch.cross(v1 - v0, v2 - v0)) face_normal_indices = (torch.arange(0, face_normals.shape[0], dtype=torch.int64, device='cuda')[:, None]).repeat(1, 3) gb_geometric_normal, _ = interpolate(face_normals[None, ...], rast_out_s, face_normal_indices.int()) if flat_shading: gb_normal = mesh.f_nrm[rast_out_s[:, :, :, -1].long() - 1] # empty triangle get id=0; the first idx starts from 1 gb_normal[rast_out_s[:, :, :, -1].long() == 0] = 0 else: assert mesh.v_nrm is not None gb_normal, _ = interpolate(mesh.v_nrm[None, ...], rast_out_s, mesh.t_nrm_idx.int()) if mesh.v_tng is not None: gb_tangent, _ = interpolate(mesh.v_tng[None, ...], rast_out_s, mesh.t_tng_idx.int()) # Interpolate tangents else: gb_tangent = None # Do not use texture coordinate in our case gb_texc, gb_texc_deriv = None, None ################################################################################ # Shade ################################################################################ buffers = shade(gb_pos, gb_geometric_normal, gb_normal, gb_tangent, gb_texc, gb_texc_deriv, view_pos, lgt, mesh.material, bsdf, xfm_lgt=xfm_lgt) #### get a mask on mesh (used to identify foreground) mask_cont, _ = interpolate(torch.ones_like(mesh.v_pos[None, :, :1], device=mesh.v_pos.device), rast_out_s, mesh.t_pos_idx.int()) mask = (mask_cont > 0).float() buffers['mask'] = mask buffers['mask_cont'] = mask_cont ################################################################################ # Prepare output ################################################################################ # Scale back up to visibility resolution if using MSAA if spp > 1 and msaa: for key in buffers.keys(): if buffers[key] is not None: buffers[key] = util.scale_img_nhwc(buffers[key], full_res, mag='nearest', min='nearest') # Return buffers return buffers # ============================================================================================== # Render a depth peeled mesh (scene), some limitations: # - Single mesh # - Single light # - Single material # ============================================================================================== def render_mesh( ctx, mesh, mtx_in, view_pos, lgt, resolution, spp = 1, num_layers = 1, msaa = False, background = None, bsdf = None, xfm_lgt = None, tet_centers = None, flat_shading = False ): def prepare_input_vector(x): x = torch.tensor(x, dtype=torch.float32, device='cuda') if not torch.is_tensor(x) else x return x[:, None, None, :] if len(x.shape) == 2 else x def composite_buffer(key, layers, background, antialias): accum = background for buffers, rast in layers: alpha = (rast[..., -1:] > 0).float() * buffers[key][..., -1:] accum = torch.lerp(accum, torch.cat((buffers[key][..., :-1], torch.ones_like(buffers[key][..., -1:])), dim=-1), alpha) if antialias: accum = dr.antialias(accum.contiguous(), rast, v_pos_clip, mesh.t_pos_idx.int()) break ## HACK: the first layer only return accum def separate_buffer(key, layers, background, antialias): accum_list = [] for buffers, rast in layers: accum = background alpha = (rast[..., -1:] > 0).float() * buffers[key][..., -1:] accum = torch.lerp(accum, torch.cat((buffers[key][..., :-1], torch.ones_like(buffers[key][..., -1:])), dim=-1), alpha) if antialias: accum = dr.antialias(accum.contiguous(), rast, v_pos_clip, mesh.t_pos_idx.int()) accum_list.append(accum) return accum_list assert mesh.t_pos_idx.shape[0] > 0, "Got empty training triangle mesh (unrecoverable discontinuity)" assert background is None or (background.shape[1] == resolution[0] and background.shape[2] == resolution[1]) full_res = [resolution[0]*spp, resolution[1]*spp] # Convert numpy arrays to torch tensors mtx_in = torch.tensor(mtx_in, dtype=torch.float32, device='cuda') if not torch.is_tensor(mtx_in) else mtx_in view_pos = prepare_input_vector(view_pos) # clip space transform v_pos_clip = ru.xfm_points(mesh.v_pos[None, ...], mtx_in) # Render all layers front-to-back with dr.DepthPeeler(ctx, v_pos_clip, mesh.t_pos_idx.int(), full_res) as peeler: rast, db = peeler.rasterize_next_layer() layers = [(render_layer(rast, db, mesh, view_pos, lgt, resolution, spp, msaa, bsdf, xfm_lgt, flat_shading), rast)] rast_1st_layer = rast # with torch.no_grad(): if True: rast, db = peeler.rasterize_next_layer() layers2 = [(render_layer(rast, db, mesh, view_pos, lgt, resolution, spp, msaa, bsdf, xfm_lgt, flat_shading), rast)] # Setup background if background is not None: if spp > 1: background = util.scale_img_nhwc(background, full_res, mag='nearest', min='nearest') background = torch.cat((background, torch.zeros_like(background[..., 0:1])), dim=-1) else: background = torch.zeros(1, full_res[0], full_res[1], 4, dtype=torch.float32, device='cuda') # Composite layers front-to-back out_buffers = {} for key in layers[0][0].keys(): if key == 'shaded': accum = composite_buffer(key, layers, background, True) elif (key == 'depth' or key == 'pos') and layers[0][0][key] is not None: accum = separate_buffer(key, layers, torch.ones_like(layers[0][0][key]) * 20.0, False) elif ('normal' in key) and layers[0][0][key] is not None: accum = composite_buffer(key, layers, torch.zeros_like(layers[0][0][key]), True) elif layers[0][0][key] is not None: accum = composite_buffer(key, layers, torch.zeros_like(layers[0][0][key]), False) if (key == 'depth' or key == 'pos') and layers[0][0][key] is not None: out_buffers[key] = util.avg_pool_nhwc(accum[0], spp) if spp > 1 else accum[0] else: # Downscale to framebuffer resolution. Use avg pooling out_buffers[key] = util.avg_pool_nhwc(accum, spp) if spp > 1 else accum accum = composite_buffer('shaded', layers, background, True) out_buffers['shaded_second'] = util.avg_pool_nhwc(accum, spp) if spp > 1 else accum accum = separate_buffer('depth', layers2, -1 * torch.ones_like(layers2[0][0]['depth']), False) out_buffers['depth_second'] = util.avg_pool_nhwc(accum[0], spp) if spp > 1 else accum[0] accum = separate_buffer('normal', layers2, torch.zeros_like(layers2[0][0]['normal']), False) out_buffers['normal_second'] = util.avg_pool_nhwc(accum[0], spp) if spp > 1 else accum[0] rast_triangle_id = rast_1st_layer[:, :, :, -1].unique() if rast_triangle_id[0] == 0: if rast_triangle_id.size(0) > 1: rast_triangle_id = rast_triangle_id[1:] - 1 ## since by the convention of the rasterizer, 0 = empty else: rast_triangle_id = None out_buffers['rast_triangle_id'] = rast_triangle_id out_buffers['rast_depth'] = rast_1st_layer[:, :, :, -2] # z-buffer if tet_centers is not None: with torch.no_grad(): v_pos_clip = v_pos_clip[0] assert full_res[0] == full_res[1] homo_transformed_tet_centers = ru.xfm_points(tet_centers[None, ...], mtx_in) transformed_tet_centers = homo_transformed_tet_centers[0, :, :3] / homo_transformed_tet_centers[0, :, 3:4] int_transformed_tet_centers = torch.round((transformed_tet_centers / 2.0 + 0.5) * (full_res[0] - 1)).long() # from the clip space (i.e., [-1, 1]^3) to the nearest integer coordinates in the canvas ### transpose THE "image" tmp_int_transformed_tet_centers = int_transformed_tet_centers.clone() int_transformed_tet_centers[:, 0] = tmp_int_transformed_tet_centers[:, 1] int_transformed_tet_centers[:, 1] = tmp_int_transformed_tet_centers[:, 0] valid_tet_centers = ((torch.logical_and((int_transformed_tet_centers <= full_res[0] - 1), int_transformed_tet_centers >= 0).float()).prod(dim=-1) == 1) # those tet centers in/on the edge of the clip space valid_int_transformed_tet_centers = int_transformed_tet_centers[valid_tet_centers] tet_center_dirs = (tet_centers - view_pos.view(1, 3)) tet_center_depths = tet_center_dirs.pow(2).sum(-1).sqrt() ### Finding occluded tetrahedra valid_transformed_tet_center_depths = transformed_tet_centers[valid_tet_centers][:, -1] # get the depth in the clip space valid_tet_ids = torch.arange(tet_centers.size(0)).to(valid_tet_centers.device)[valid_tet_centers] corrected_rast_depth = out_buffers['rast_depth'].clone().detach() corrected_rast_depth[rast_1st_layer[:, :, :, -1] == 0] = 100 # for all pixels without any rasterized mesh, just set the depth to a large enough value ''' Hacky way of finding most of the non-occluded tetrahedra (except for already rasterized ones): For each pixel, find the min depth in a small neighborhood. If the center of a tetrahedron (coinciding with this pixel when rasterized) is smaller than this min depth, this tetrahedron is certainly non-occluded. Doing this because exact per-pixel comparison for triangular meshes can be costly, plus we do not need to perfectly finding all visible tetrahedra. ''' depth_search_range = 7 ### change this value for different resolution in rasterization corrected_rast_depth = -torch.nn.functional.max_pool2d( -corrected_rast_depth, kernel_size=2*depth_search_range+1, stride=1, padding=depth_search_range) valid_reference_depth = corrected_rast_depth[0, valid_int_transformed_tet_centers[:, 0], valid_int_transformed_tet_centers[:, 1]] depth_filter = valid_reference_depth >= valid_transformed_tet_center_depths empty_2d_mask = (rast_1st_layer[:, :, :, -1] == 0) empty_2d_mask = (-torch.nn.functional.max_pool2d( -empty_2d_mask.float(), kernel_size=2*depth_search_range+1, stride=1, padding=depth_search_range)).bool() ### similar philosophy for using a neighborhood empty_filter = empty_2d_mask[0, valid_int_transformed_tet_centers[:, 0], valid_int_transformed_tet_centers[:, 1]] ## visible tets are either determined by depth test or emptyness test out_buffers['visible_tet_id'] = valid_tet_ids[torch.logical_or(empty_filter, depth_filter)] return out_buffers # ============================================================================================== # Render UVs # ============================================================================================== def render_uv(ctx, mesh, resolution, mlp_texture): # clip space transform uv_clip = mesh.v_tex[None, ...]*2.0 - 1.0 # pad to four component coordinate uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[...,0:1]), torch.ones_like(uv_clip[...,0:1])), dim = -1) # rasterize rast, _ = dr.rasterize(ctx, uv_clip4, mesh.t_tex_idx.int(), resolution) # Interpolate world space position gb_pos, _ = interpolate(mesh.v_pos[None, ...], rast, mesh.t_pos_idx.int()) # Sample out textures from MLP all_tex = mlp_texture.sample(gb_pos) assert all_tex.shape[-1] == 9 or all_tex.shape[-1] == 10, "Combined kd_ks_normal must be 9 or 10 channels" perturbed_nrm = all_tex[..., -3:] return (rast[..., -1:] > 0).float(), all_tex[..., :-6], all_tex[..., -6:-3], util.safe_normalize(perturbed_nrm) # ============================================================================================== # Render UVs # ============================================================================================== def render_uv_nrm(ctx, mesh, resolution, mlp_texture): # clip space transform uv_clip = mesh.v_tex[None, ...]*2.0 - 1.0 # pad to four component coordinate uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[...,0:1]), torch.ones_like(uv_clip[...,0:1])), dim = -1) # rasterize rast, _ = dr.rasterize(ctx, uv_clip4, mesh.t_tex_idx.int(), resolution) # Interpolate world space position gb_pos, _ = interpolate(mesh.v_pos[None, ...], rast, mesh.t_pos_idx.int()) # Sample out textures from MLP all_tex = mlp_texture.sample(gb_pos) perturbed_nrm = all_tex[..., -3:] return (rast[..., -1:] > 0).float(), util.safe_normalize(perturbed_nrm)