triplaneturbo_executable/utils/mesh.py

import numpy as np
import torch
import torch.nn.functional as F
import trimesh

def dot(x, y):
    return torch.sum(x * y, -1, keepdim=True)

class Mesh:
    def __init__(
        self, v_pos, t_pos_idx, material=None
    ):
        self.v_pos = v_pos
        self.t_pos_idx = t_pos_idx
        self.material = material
        self._v_nrm = None
        self._v_tng = None
        self._v_tex = None
        self._t_tex_idx = None
        self._v_rgb = None
        self._edges = None
        self.extras = {}

    def add_extra(self, k, v) -> None:
        self.extras[k] = v

    def remove_outlier(self, n_face_threshold=5):
        """Remove outlier components with fewer faces than threshold."""
        # Convert to trimesh
        trimesh_mesh = self.as_trimesh()
        
        # Split into connected components
        components = trimesh_mesh.split(only_watertight=False)
        
        # Filter components with few faces
        valid_components = [c for c in components if len(c.faces) > n_face_threshold]
        
        if len(valid_components) == 0:
            # If no valid components, return the original mesh
            return self
        
        # Combine valid components
        combined = trimesh.util.concatenate(valid_components)
        
        # Convert back to our Mesh format
        new_mesh = Mesh(
            torch.tensor(combined.vertices, dtype=self.v_pos.dtype, device=self.v_pos.device),
            torch.tensor(combined.faces, dtype=self.t_pos_idx.dtype, device=self.t_pos_idx.device)
        )
        
        return new_mesh

    @property
    def requires_grad(self):
        return self.v_pos.requires_grad

    @property
    def v_nrm(self):
        if self._v_nrm is None:
            self._v_nrm = self._compute_vertex_normal()
        return self._v_nrm

    @property
    def v_tng(self):
        if self._v_tng is None:
            self._v_tng = self._compute_vertex_tangent()
        return self._v_tng

    @property
    def v_tex(self):
        if self._v_tex is None:
            self._v_tex, self._t_tex_idx = self._unwrap_uv()
        return self._v_tex

    @property
    def t_tex_idx(self):
        if self._t_tex_idx is None:
            self._v_tex, self._t_tex_idx = self._unwrap_uv()
        return self._t_tex_idx

    @property
    def v_rgb(self):
        return self._v_rgb

    @property
    def edges(self):
        if self._edges is None:
            self._edges = self._compute_edges()
        return self._edges

    def _compute_vertex_normal(self):
        i0 = self.t_pos_idx[:, 0]
        i1 = self.t_pos_idx[:, 1]
        i2 = self.t_pos_idx[:, 2]

        v0 = self.v_pos[i0, :]
        v1 = self.v_pos[i1, :]
        v2 = self.v_pos[i2, :]

        face_normals = torch.cross(v1 - v0, v2 - v0)

        # Splat face normals to vertices
        v_nrm = torch.zeros_like(self.v_pos)
        v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
        v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
        v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)

        # Normalize, replace zero (degenerated) normals with some default value
        v_nrm = torch.where(
            dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
        )
        v_nrm = F.normalize(v_nrm, dim=1)

        if torch.is_anomaly_enabled():
            assert torch.all(torch.isfinite(v_nrm))

        return v_nrm

    def _compute_vertex_tangent(self):
        vn_idx = [None] * 3
        pos = [None] * 3
        tex = [None] * 3
        for i in range(0, 3):
            pos[i] = self.v_pos[self.t_pos_idx[:, i]]
            tex[i] = self.v_tex[self.t_tex_idx[:, i]]
            # t_nrm_idx is always the same as t_pos_idx
            vn_idx[i] = self.t_pos_idx[:, i]

        tangents = torch.zeros_like(self.v_nrm)
        tansum = torch.zeros_like(self.v_nrm)

        # Compute tangent space for each triangle
        uve1 = tex[1] - tex[0]
        uve2 = tex[2] - tex[0]
        pe1 = pos[1] - pos[0]
        pe2 = pos[2] - pos[0]

        nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]
        denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]

        # Avoid division by zero for degenerated texture coordinates
        tang = nom / torch.where(
            denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)
        )

        # Update all 3 vertices
        for i in range(0, 3):
            idx = vn_idx[i][:, None].repeat(1, 3)
            tangents.scatter_add_(0, idx, tang)  # tangents[n_i] = tangents[n_i] + tang
            tansum.scatter_add_(
                0, idx, torch.ones_like(tang)
            )  # tansum[n_i] = tansum[n_i] + 1
        tangents = tangents / tansum

        # Normalize and make sure tangent is perpendicular to normal
        tangents = F.normalize(tangents, dim=1)
        tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)

        if torch.is_anomaly_enabled():
            assert torch.all(torch.isfinite(tangents))

        return tangents

    def _unwrap_uv(
        self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}
    ):

        import xatlas

        atlas = xatlas.Atlas()
        atlas.add_mesh(
            self.v_pos.detach().cpu().numpy(),
            self.t_pos_idx.cpu().numpy(),
        )
        co = xatlas.ChartOptions()
        po = xatlas.PackOptions()
        for k, v in xatlas_chart_options.items():
            setattr(co, k, v)
        for k, v in xatlas_pack_options.items():
            setattr(po, k, v)
        atlas.generate(co, po)
        vmapping, indices, uvs = atlas.get_mesh(0)
        vmapping = (
            torch.from_numpy(
                vmapping.astype(np.uint64, casting="same_kind").view(np.int64)
            )
            .to(self.v_pos.device)
            .long()
        )
        uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()
        indices = (
            torch.from_numpy(
                indices.astype(np.uint64, casting="same_kind").view(np.int64)
            )
            .to(self.v_pos.device)
            .long()
        )
        return uvs, indices

    def unwrap_uv(
        self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}
    ):
        self._v_tex, self._t_tex_idx = self._unwrap_uv(
            xatlas_chart_options, xatlas_pack_options
        )

    def set_vertex_color(self, v_rgb):
        assert v_rgb.shape[0] == self.v_pos.shape[0]
        self._v_rgb = v_rgb

    def _compute_edges(self):
        # Compute edges
        edges = torch.cat(
            [
                self.t_pos_idx[:, [0, 1]],
                self.t_pos_idx[:, [1, 2]],
                self.t_pos_idx[:, [2, 0]],
            ],
            dim=0,
        )
        edges = edges.sort()[0]
        edges = torch.unique(edges, dim=0)
        return edges

    def normal_consistency(self):
        edge_nrm = self.v_nrm[self.edges]
        nc = (
            1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)
        ).mean()
        return nc

    def _laplacian_uniform(self):
        # from stable-dreamfusion
        # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224
        verts, faces = self.v_pos, self.t_pos_idx

        V = verts.shape[0]
        F = faces.shape[0]

        # Neighbor indices
        ii = faces[:, [1, 2, 0]].flatten()
        jj = faces[:, [2, 0, 1]].flatten()
        adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(
            dim=1
        )
        adj_values = torch.ones(adj.shape[1]).to(verts)

        # Diagonal indices
        diag_idx = adj[0]

        # Build the sparse matrix
        idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)
        values = torch.cat((-adj_values, adj_values))

        # The coalesce operation sums the duplicate indices, resulting in the
        # correct diagonal
        return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()

    def laplacian(self):
        with torch.no_grad():
            L = self._laplacian_uniform()
        loss = L.mm(self.v_pos)
        loss = loss.norm(dim=1)
        loss = loss.mean()
        return loss

    def to(self, device):
        v_pos = self.v_pos.to(device)
        t_pos_idx = self.t_pos_idx.to(device)
        return Mesh(v_pos, t_pos_idx)
    
    def as_trimesh(self):
        vertices = self.v_pos.detach().cpu().numpy()
        faces = self.t_pos_idx.detach().cpu().numpy()
        
        mesh = trimesh.Trimesh(
            vertices=vertices,
            faces=faces,
            process=False
        )
        
        # Add texture if available
        if hasattr(self, 'albedo_map') and self.albedo_map is not None:
            # Create texture visuals
            uv = self.v_tex.detach().cpu().numpy()
            
            # Create texture visuals
            visual = trimesh.visual.texture.TextureVisuals(
                uv=uv,
                material=trimesh.visual.material.SimpleMaterial()
            )
            mesh.visual = visual
            
        return mesh

def scale_tensor(x, input_range, target_range):
    """Scale tensor from input_range to target_range."""
    x_unit = (x - input_range[0]) / (input_range[1] - input_range[0])
    x_scaled = x_unit * (target_range[1] - target_range[0]) + target_range[0]
    return x_scaled