threestudio/models/geometry/implicit_volume.py [149:183]:
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                if self.cfg.normal_type == "finite_difference_laplacian":
                    offsets: Float[Tensor, "6 3"] = torch.as_tensor(
                        [
                            [eps, 0.0, 0.0],
                            [-eps, 0.0, 0.0],
                            [0.0, eps, 0.0],
                            [0.0, -eps, 0.0],
                            [0.0, 0.0, eps],
                            [0.0, 0.0, -eps],
                        ]
                    ).to(points_unscaled)
                    points_offset: Float[Tensor, "... 6 3"] = (
                        points_unscaled[..., None, :] + offsets
                    ).clamp(-self.cfg.radius, self.cfg.radius)
                    density_offset: Float[Tensor, "... 6 1"] = self.forward_density(
                        points_offset
                    )
                    normal = (
                        -0.5
                        * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])
                        / eps
                    )
                else:
                    offsets: Float[Tensor, "3 3"] = torch.as_tensor(
                        [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]
                    ).to(points_unscaled)
                    points_offset: Float[Tensor, "... 3 3"] = (
                        points_unscaled[..., None, :] + offsets
                    ).clamp(-self.cfg.radius, self.cfg.radius)
                    density_offset: Float[Tensor, "... 3 1"] = self.forward_density(
                        points_offset
                    )
                    normal = -(density_offset[..., 0::1, 0] - density) / eps
                normal = F.normalize(normal, dim=-1)
            elif self.cfg.normal_type == "pred":
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threestudio/models/geometry/volume_grid.py [106:140]:
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                if self.cfg.normal_type == "finite_difference_laplacian":
                    offsets: Float[Tensor, "6 3"] = torch.as_tensor(
                        [
                            [eps, 0.0, 0.0],
                            [-eps, 0.0, 0.0],
                            [0.0, eps, 0.0],
                            [0.0, -eps, 0.0],
                            [0.0, 0.0, eps],
                            [0.0, 0.0, -eps],
                        ]
                    ).to(points_unscaled)
                    points_offset: Float[Tensor, "... 6 3"] = (
                        points_unscaled[..., None, :] + offsets
                    ).clamp(-self.cfg.radius, self.cfg.radius)
                    density_offset: Float[Tensor, "... 6 1"] = self.forward_density(
                        points_offset
                    )
                    normal = (
                        -0.5
                        * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])
                        / eps
                    )
                else:
                    offsets: Float[Tensor, "3 3"] = torch.as_tensor(
                        [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]
                    ).to(points_unscaled)
                    points_offset: Float[Tensor, "... 3 3"] = (
                        points_unscaled[..., None, :] + offsets
                    ).clamp(-self.cfg.radius, self.cfg.radius)
                    density_offset: Float[Tensor, "... 3 1"] = self.forward_density(
                        points_offset
                    )
                    normal = -(density_offset[..., 0::1, 0] - density) / eps
                normal = F.normalize(normal, dim=-1)
            elif self.cfg.normal_type == "pred":
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