triplaneturbo_executable/utils/mesh_exporter.py

from dataclasses import dataclass

import torch
import torch.nn as nn
import os
import numpy as np
from .saving import SaverMixin

from ..utils.mesh import Mesh
from ..utils.general_utils import scale_tensor

@dataclass
class ExporterOutput:
    save_name: str
    save_type: str
    params: dict


class IsosurfaceHelper(nn.Module):
    points_range = (0, 1)

    @property
    def grid_vertices(self):
        raise NotImplementedError
    
class DiffMarchingCubeHelper(IsosurfaceHelper):
    def __init__(
            self, 
            resolution, 
            point_range = (0, 1)
        ):
        super().__init__()
        self.resolution = resolution
        self.points_range = point_range

        from diso import DiffMC
        self.mc_func = DiffMC(dtype=torch.float32)
        self._grid_vertices = None
        self.register_buffer(
            "_dummy", torch.zeros(0, dtype=torch.float32), persistent=False
        )

    @property
    def grid_vertices(self):
        if self._grid_vertices is None:
            # keep the vertices on CPU so that we can support very large resolution
            x, y, z = (
                torch.linspace(*self.points_range, self.resolution),
                torch.linspace(*self.points_range, self.resolution),
                torch.linspace(*self.points_range, self.resolution),
            )
            x, y, z = torch.meshgrid(x, y, z, indexing="ij")
            verts = torch.stack([x, y, z], dim=-1).reshape(-1, 3)
            verts = verts * (self.points_range[1] - self.points_range[0]) + self.points_range[0]

            self._grid_vertices = verts
        return self._grid_vertices

    def forward(
        self,
        level,
        deformation = None,
        isovalue=0.0,
    ):
        level = level.view(self.resolution, self.resolution, self.resolution)
        if deformation is not None:
            deformation = deformation.view(self.resolution, self.resolution, self.resolution, 3)
        v_pos, t_pos_idx = self.mc_func(level, deformation, isovalue=isovalue)
        v_pos = v_pos * (self.points_range[1] - self.points_range[0]) + self.points_range[0]
        # TODO: if the mesh is good
        return Mesh(v_pos=v_pos, t_pos_idx=t_pos_idx)


def isosurface(
        space_cache,
        forward_field,
        isosurface_helper,
    ):

    # the isosurface is dependent on the space cache
    # randomly detach isosurface method if it is differentiable
    # get the batchsize
    if torch.is_tensor(space_cache): #space cache
        batch_size = space_cache.shape[0]
    elif isinstance(space_cache, dict): #hyper net
        # Dict[str, List[Float[Tensor, "B ..."]]]
        for key in space_cache.keys():
            batch_size = space_cache[key][0].shape[0]
            break

    # scale the points to [-1, 1]
    points = scale_tensor(
        isosurface_helper.grid_vertices.to(space_cache.device),
        isosurface_helper.points_range,
        [-1, 1], # hard coded isosurface_bbox
    )
    # get the sdf values    
    sdf_batch, deformation_batch = forward_field(
        points[None, ...].expand(batch_size, -1, -1),
        space_cache
    )
    
    # get the isosurface
    mesh_list = []

    # check if the sdf is empty
    # for sdf, deformation in zip(sdf_batch, deformation_batch):
    for index in range(sdf_batch.shape[0]):
        sdf = sdf_batch[index]

        # the deformation may be None
        if deformation_batch is None:
            deformation = None
        else:
            deformation = deformation_batch[index]

        # special case when all sdf values are positive or negative, thus no isosurface
        if torch.all(sdf > 0) or torch.all(sdf < 0):
            
            print(f"All sdf values are positive or negative, no isosurface")
            sdf = torch.norm(points, dim=-1) - 1

        mesh = isosurface_helper(sdf, deformation)
        
        mesh.v_pos = scale_tensor(
            mesh.v_pos,
            isosurface_helper.points_range,
            [-1, 1], # hard coded isosurface_bbox
        )

        # TODO: implement outlier removal        
        # if cfg.isosurface_remove_outliers:
        #     mesh = mesh.remove_outlier(cfg.isosurface_outlier_n_faces_threshold)

        mesh_list.append(mesh)
        
    return mesh_list

def colorize_mesh(
    space_cache,
    export_fn,
    mesh_list,
    activation,
):
    """Colorize the mesh using the geometry's export function and space cache.
    
    Args:
        space_cache: The space cache containing feature information
        export_fn: The export function from geometry that generates features
        mesh_list: List of meshes to colorize
        
    Returns:
        List[Mesh]: List of colorized meshes
    """
    # Process each mesh in the batch
    for i, mesh in enumerate(mesh_list):
        # Get vertex positions
        points = mesh.v_pos[None, ...]  # Add batch dimension [1, N, 3]
        
        # Get the corresponding space cache slice for this mesh
        if torch.is_tensor(space_cache):
            space_cache_slice = space_cache[i:i+1]
        elif isinstance(space_cache, dict):
            space_cache_slice = {}
            for key in space_cache.keys():
                space_cache_slice[key] = [
                    weight[i:i+1] for weight in space_cache[key]
                ]
        
        # Export features for the vertices
        out = export_fn(points, space_cache_slice)
        
        # Update vertex colors if features exist
        if "features" in out:
            features = out["features"].squeeze(0)  # Remove batch dim [N, C]
            # Convert features to RGB colors
            mesh._v_rgb = activation(features)  # Access private attribute directly
            
    return mesh_list

class MeshExporter(SaverMixin):
    def __init__(self, save_dir="outputs"):
        self.save_dir = save_dir
        os.makedirs(save_dir, exist_ok=True)

    def get_save_dir(self):
        return self.save_dir

    def get_save_path(self, filename):
        return os.path.join(self.save_dir, filename)

    def convert_data(self, x):
        if isinstance(x, torch.Tensor):
            return x.detach().cpu().numpy()
        return x

def export_obj(
        mesh, 
        save_path,
        save_normal = False,
    ):
    """
    Export mesh data to OBJ file format.
    
    Args:
        mesh_data: Dictionary containing mesh data (vertices, faces, etc.)
        save_path: Path to save the OBJ file
        
    Returns:
        List of saved file paths
    """

    # Create exporter
    exporter = MeshExporter(os.path.dirname(save_path))
    
    # Export mesh
    save_paths = exporter.save_obj(
        os.path.basename(save_path),
        mesh,
        save_mat=None,
        save_normal=save_normal and mesh.v_nrm is not None,
        save_uv=False,
        save_vertex_color=mesh.v_rgb is not None,
    )
    
    return save_paths