trellis/representations/mesh/cube2mesh.py

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
import torch
from ...modules.sparse import SparseTensor
from easydict import EasyDict as edict
from .utils_cube import *
from .flexicube import FlexiCubes


class MeshExtractResult:
    def __init__(self, vertices, faces, vertex_attrs=None, res=64):
        self.vertices = vertices
        self.faces = faces.long()
        self.vertex_attrs = vertex_attrs
        self.face_normal = self.comput_face_normals(vertices, faces)
        self.res = res
        self.success = vertices.shape[0] != 0 and faces.shape[0] != 0

        # training only
        self.tsdf_v = None
        self.tsdf_s = None
        self.reg_loss = None

    def comput_face_normals(self, verts, faces):
        i0 = faces[..., 0].long()
        i1 = faces[..., 1].long()
        i2 = faces[..., 2].long()

        v0 = verts[i0, :]
        v1 = verts[i1, :]
        v2 = verts[i2, :]
        face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
        face_normals = torch.nn.functional.normalize(face_normals, dim=1)
        # print(face_normals.min(), face_normals.max(), face_normals.shape)
        return face_normals[:, None, :].repeat(1, 3, 1)

    def comput_v_normals(self, verts, faces):
        i0 = faces[..., 0].long()
        i1 = faces[..., 1].long()
        i2 = faces[..., 2].long()

        v0 = verts[i0, :]
        v1 = verts[i1, :]
        v2 = verts[i2, :]
        face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
        v_normals = torch.zeros_like(verts)
        v_normals.scatter_add_(0, i0[..., None].repeat(1, 3), face_normals)
        v_normals.scatter_add_(0, i1[..., None].repeat(1, 3), face_normals)
        v_normals.scatter_add_(0, i2[..., None].repeat(1, 3), face_normals)

        v_normals = torch.nn.functional.normalize(v_normals, dim=1)
        return v_normals


class SparseFeatures2Mesh:
    def __init__(self, device="cuda", res=64, use_color=True):
        """
        a model to generate a mesh from sparse features structures using flexicube
        """
        super().__init__()
        self.device = device
        self.res = res
        self.mesh_extractor = FlexiCubes(device=device)
        self.sdf_bias = -1.0 / res
        verts, cube = construct_dense_grid(self.res, self.device)
        self.reg_c = cube.to(self.device)
        self.reg_v = verts.to(self.device)
        self.use_color = use_color
        self._calc_layout()

    def _calc_layout(self):
        LAYOUTS = {
            "sdf": {"shape": (8, 1), "size": 8},
            "deform": {"shape": (8, 3), "size": 8 * 3},
            "weights": {"shape": (21,), "size": 21},
        }
        if self.use_color:
            """
            6 channel color including normal map
            """
            LAYOUTS["color"] = {
                "shape": (
                    8,
                    6,
                ),
                "size": 8 * 6,
            }
        self.layouts = edict(LAYOUTS)
        start = 0
        for k, v in self.layouts.items():
            v["range"] = (start, start + v["size"])
            start += v["size"]
        self.feats_channels = start

    def get_layout(self, feats: torch.Tensor, name: str):
        if name not in self.layouts:
            return None
        return feats[
            :, self.layouts[name]["range"][0] : self.layouts[name]["range"][1]
        ].reshape(-1, *self.layouts[name]["shape"])

    def __call__(self, cubefeats: SparseTensor, training=False):
        """
        Generates a mesh based on the specified sparse voxel structures.
        Args:
            cube_attrs [Nx21] : Sparse Tensor attrs about cube weights
            verts_attrs [Nx10] : [0:1] SDF [1:4] deform [4:7] color [7:10] normal
        Returns:
            return the success tag and ni you loss,
        """
        # add sdf bias to verts_attrs
        coords = cubefeats.coords[:, 1:]
        feats = cubefeats.feats

        sdf, deform, color, weights = [
            self.get_layout(feats, name)
            for name in ["sdf", "deform", "color", "weights"]
        ]
        sdf += self.sdf_bias
        v_attrs = [sdf, deform, color] if self.use_color else [sdf, deform]
        v_pos, v_attrs, reg_loss = sparse_cube2verts(
            coords, torch.cat(v_attrs, dim=-1), training=training
        )
        v_attrs_d = get_dense_attrs(v_pos, v_attrs, res=self.res + 1, sdf_init=True)
        weights_d = get_dense_attrs(coords, weights, res=self.res, sdf_init=False)
        if self.use_color:
            sdf_d, deform_d, colors_d = (
                v_attrs_d[..., 0],
                v_attrs_d[..., 1:4],
                v_attrs_d[..., 4:],
            )
        else:
            sdf_d, deform_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4]
            colors_d = None

        x_nx3 = get_defomed_verts(self.reg_v, deform_d, self.res)

        vertices, faces, L_dev, colors = self.mesh_extractor(
            voxelgrid_vertices=x_nx3,
            scalar_field=sdf_d,
            cube_idx=self.reg_c,
            resolution=self.res,
            beta=weights_d[:, :12],
            alpha=weights_d[:, 12:20],
            gamma_f=weights_d[:, 20],
            voxelgrid_colors=colors_d,
            training=training,
        )

        mesh = MeshExtractResult(
            vertices=vertices, faces=faces, vertex_attrs=colors, res=self.res
        )
        if training:
            if mesh.success:
                reg_loss += L_dev.mean() * 0.5
            reg_loss += (weights[:, :20]).abs().mean() * 0.2
            mesh.reg_loss = reg_loss
            mesh.tsdf_v = get_defomed_verts(v_pos, v_attrs[:, 1:4], self.res)
            mesh.tsdf_s = v_attrs[:, 0]
        return mesh