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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])
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 = dr.RasterizeGLContext(output_db=False, device=device)
_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
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