threestudio/models/geometry/implicit_sdf.py [146:199]:
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            centroid = mesh.vertices.mean(0)
            mesh.vertices = mesh.vertices - centroid

            # align to up-z and front-x
            dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
            dir2vec = {
                "+x": np.array([1, 0, 0]),
                "+y": np.array([0, 1, 0]),
                "+z": np.array([0, 0, 1]),
                "-x": np.array([-1, 0, 0]),
                "-y": np.array([0, -1, 0]),
                "-z": np.array([0, 0, -1]),
            }
            if (
                self.cfg.shape_init_mesh_up not in dirs
                or self.cfg.shape_init_mesh_front not in dirs
            ):
                raise ValueError(
                    f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
                )
            if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
                raise ValueError(
                    "shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
                )
            z_, x_ = (
                dir2vec[self.cfg.shape_init_mesh_up],
                dir2vec[self.cfg.shape_init_mesh_front],
            )
            y_ = np.cross(z_, x_)
            std2mesh = np.stack([x_, y_, z_], axis=0).T
            mesh2std = np.linalg.inv(std2mesh)

            # scaling
            scale = np.abs(mesh.vertices).max()
            mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
            mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T

            from pysdf import SDF

            sdf = SDF(mesh.vertices, mesh.faces)

            def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
                # add a negative signed here
                # as in pysdf the inside of the shape has positive signed distance
                return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
                    points_rand
                )[..., None]

            get_gt_sdf = func

        else:
            raise ValueError(
                f"Unknown shape initialization type: {self.cfg.shape_init}"
            )
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threestudio/models/geometry/tetrahedra_sdf_grid.py [169:222]:
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            centroid = mesh.vertices.mean(0)
            mesh.vertices = mesh.vertices - centroid

            # align to up-z and front-x
            dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
            dir2vec = {
                "+x": np.array([1, 0, 0]),
                "+y": np.array([0, 1, 0]),
                "+z": np.array([0, 0, 1]),
                "-x": np.array([-1, 0, 0]),
                "-y": np.array([0, -1, 0]),
                "-z": np.array([0, 0, -1]),
            }
            if (
                self.cfg.shape_init_mesh_up not in dirs
                or self.cfg.shape_init_mesh_front not in dirs
            ):
                raise ValueError(
                    f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
                )
            if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
                raise ValueError(
                    "shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
                )
            z_, x_ = (
                dir2vec[self.cfg.shape_init_mesh_up],
                dir2vec[self.cfg.shape_init_mesh_front],
            )
            y_ = np.cross(z_, x_)
            std2mesh = np.stack([x_, y_, z_], axis=0).T
            mesh2std = np.linalg.inv(std2mesh)

            # scaling
            scale = np.abs(mesh.vertices).max()
            mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
            mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T

            from pysdf import SDF

            sdf = SDF(mesh.vertices, mesh.faces)

            def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
                # add a negative signed here
                # as in pysdf the inside of the shape has positive signed distance
                return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
                    points_rand
                )[..., None]

            get_gt_sdf = func

        else:
            raise ValueError(
                f"Unknown shape initialization type: {self.cfg.shape_init}"
            )
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