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| # modified from https://github.com/Profactor/continuous-remeshing | |
| import nvdiffrast.torch as dr | |
| import torch | |
| from typing import Tuple | |
| def _warmup(glctx, device=None): | |
| device = 'cuda' if device is None else device | |
| #windows workaround for https://github.com/NVlabs/nvdiffrast/issues/59 | |
| def tensor(*args, **kwargs): | |
| return torch.tensor(*args, device=device, **kwargs) | |
| pos = tensor([[[-0.8, -0.8, 0, 1], [0.8, -0.8, 0, 1], [-0.8, 0.8, 0, 1]]], dtype=torch.float32) | |
| tri = tensor([[0, 1, 2]], dtype=torch.int32) | |
| dr.rasterize(glctx, pos, tri, resolution=[256, 256]) | |
| glctx = dr.RasterizeGLContext(output_db=False, device="cuda") | |
| class NormalsRenderer: | |
| _glctx:dr.RasterizeGLContext = None | |
| def __init__( | |
| self, | |
| mv: torch.Tensor, #C,4,4 | |
| proj: torch.Tensor, #C,4,4 | |
| image_size: Tuple[int,int], | |
| mvp = None, | |
| device=None, | |
| ): | |
| if mvp is None: | |
| self._mvp = proj @ mv #C,4,4 | |
| else: | |
| self._mvp = mvp | |
| self._image_size = image_size | |
| self._glctx = glctx | |
| _warmup(self._glctx, device) | |
| def render(self, | |
| vertices: torch.Tensor, #V,3 float | |
| normals: torch.Tensor, #V,3 float in [-1, 1] | |
| faces: torch.Tensor, #F,3 long | |
| ) ->torch.Tensor: #C,H,W,4 | |
| V = vertices.shape[0] | |
| faces = faces.type(torch.int32) | |
| vert_hom = torch.cat((vertices, torch.ones(V,1,device=vertices.device)),axis=-1) #V,3 -> V,4 | |
| vertices_clip = vert_hom @ self._mvp.transpose(-2,-1) #C,V,4 | |
| rast_out,_ = dr.rasterize(self._glctx, vertices_clip, faces, resolution=self._image_size, grad_db=False) #C,H,W,4 | |
| vert_col = (normals+1)/2 #V,3 | |
| col,_ = dr.interpolate(vert_col, rast_out, faces) #C,H,W,3 | |
| alpha = torch.clamp(rast_out[..., -1:], max=1) #C,H,W,1 | |
| col = torch.concat((col,alpha),dim=-1) #C,H,W,4 | |
| col = dr.antialias(col, rast_out, vertices_clip, faces) #C,H,W,4 | |
| return col #C,H,W,4 | |