hy3dshape/surface_loaders.py

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import numpy as np

import torch
import trimesh


def normalize_mesh(mesh, scale=0.9999):
    """
    Normalize the mesh to fit inside a centered cube with a specified scale.

    The mesh is translated so that its bounding box center is at the origin,
    then uniformly scaled so that the longest side of the bounding box fits within [-scale, scale].

    Args:
        mesh (trimesh.Trimesh): Input mesh to normalize.
        scale (float, optional): Scaling factor to slightly shrink the mesh inside the unit cube. Default is 0.9999.

    Returns:
        trimesh.Trimesh: The normalized mesh with applied translation and scaling.
    """
    bbox = mesh.bounds
    center = (bbox[1] + bbox[0]) / 2
    scale_ = (bbox[1] - bbox[0]).max()

    mesh.apply_translation(-center)
    mesh.apply_scale(1 / scale_ * 2 * scale)

    return mesh


def sample_pointcloud(mesh, num=200000):
    """
    Sample points uniformly from the surface of the mesh along with their corresponding face normals.

    Args:
        mesh (trimesh.Trimesh): Input mesh to sample from.
        num (int, optional): Number of points to sample. Default is 200000.

    Returns:
        Tuple[torch.Tensor, torch.Tensor]:
            - points: Sampled points as a float tensor of shape (num, 3).
            - normals: Corresponding normals as a float tensor of shape (num, 3).
    """
    points, face_idx = mesh.sample(num, return_index=True)
    normals = mesh.face_normals[face_idx]
    points = torch.from_numpy(points.astype(np.float32))
    normals = torch.from_numpy(normals.astype(np.float32))
    return points, normals


def load_surface(mesh, num_points=8192):
    """
    Normalize the mesh, sample points and normals from its surface, and randomly select a subset.

    Args:
        mesh (trimesh.Trimesh): Input mesh to process.
        num_points (int, optional): Number of points to randomly select 
                from the sampled surface points. Default is 8192.

    Returns:
        Tuple[torch.Tensor, trimesh.Trimesh]:
            - surface: Tensor of shape (1, num_points, 6), concatenating points and normals.
            - mesh: The normalized mesh.
    """

    mesh = normalize_mesh(mesh, scale=0.98)
    surface, normal = sample_pointcloud(mesh)

    rng = np.random.default_rng(seed=0)
    ind = rng.choice(surface.shape[0], num_points, replace=False)
    surface = torch.FloatTensor(surface[ind])
    normal = torch.FloatTensor(normal[ind])

    surface = torch.cat([surface, normal], dim=-1).unsqueeze(0)

    return surface, mesh


def sharp_sample_pointcloud(mesh, num=16384):
    """
    Sample points and normals preferentially from sharp edges of the mesh.

    Sharp edges are detected based on the angle between vertex normals and face normals.
    Points are sampled along these edges proportionally to edge length.

    Args:
        mesh (trimesh.Trimesh): Input mesh to sample from.
        num (int, optional): Number of points to sample from sharp edges. Default is 16384.

    Returns:
        Tuple[np.ndarray, np.ndarray]:
            - samples: Sampled points along sharp edges, shape (num, 3).
            - normals: Corresponding interpolated normals, shape (num, 3).
    """
    V = mesh.vertices
    N = mesh.face_normals
    VN = mesh.vertex_normals
    F = mesh.faces
    VN2 = np.ones(V.shape[0])
    for i in range(3):
        dot = np.stack((VN2[F[:, i]], np.sum(VN[F[:, i]] * N, axis=-1)), axis=-1)
        VN2[F[:, i]] = np.min(dot, axis=-1)

    sharp_mask = VN2 < 0.985
    # collect edge
    edge_a = np.concatenate((F[:, 0], F[:, 1], F[:, 2]))
    edge_b = np.concatenate((F[:, 1], F[:, 2], F[:, 0]))
    sharp_edge = ((sharp_mask[edge_a] * sharp_mask[edge_b]))
    edge_a = edge_a[sharp_edge > 0]
    edge_b = edge_b[sharp_edge > 0]

    sharp_verts_a = V[edge_a]
    sharp_verts_b = V[edge_b]
    sharp_verts_an = VN[edge_a]
    sharp_verts_bn = VN[edge_b]

    weights = np.linalg.norm(sharp_verts_b - sharp_verts_a, axis=-1)
    weights /= np.sum(weights)

    random_number = np.random.rand(num)
    w = np.random.rand(num, 1)
    index = np.searchsorted(weights.cumsum(), random_number)
    samples = w * sharp_verts_a[index] + (1 - w) * sharp_verts_b[index]
    normals = w * sharp_verts_an[index] + (1 - w) * sharp_verts_bn[index]
    return samples, normals


def load_surface_sharpegde(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag=True):
    try:
        mesh_full = trimesh.util.concatenate(mesh.dump())
    except Exception as err:
        mesh_full = trimesh.util.concatenate(mesh)
    mesh_full = normalize_mesh(mesh_full)

    origin_num = mesh_full.faces.shape[0]
    original_vertices = mesh_full.vertices
    original_faces = mesh_full.faces

    mesh = trimesh.Trimesh(vertices=original_vertices, faces=original_faces[:origin_num])
    mesh_fill = trimesh.Trimesh(vertices=original_vertices, faces=original_faces[origin_num:])
    area = mesh.area
    area_fill = mesh_fill.area
    sample_num = 499712 // 2
    num_fill = int(sample_num * (area_fill / (area + area_fill)))
    num = sample_num - num_fill

    random_surface, random_normal = sample_pointcloud(mesh, num=num)
    if num_fill == 0:
        random_surface_fill, random_normal_fill = np.zeros((0, 3)), np.zeros((0, 3))
    else:
        random_surface_fill, random_normal_fill = sample_pointcloud(mesh_fill, num=num_fill)
    random_sharp_surface, sharp_normal = sharp_sample_pointcloud(mesh, num=sample_num)

    # save_surface
    surface = np.concatenate((random_surface, random_normal), axis=1).astype(np.float16)
    surface_fill = np.concatenate((random_surface_fill, random_normal_fill), axis=1).astype(np.float16)
    sharp_surface = np.concatenate((random_sharp_surface, sharp_normal), axis=1).astype(np.float16)
    surface = np.concatenate((surface, surface_fill), axis=0)
    if sharpedge_flag:
        sharpedge_label = np.zeros((surface.shape[0], 1))
        surface = np.concatenate((surface, sharpedge_label), axis=1)
        sharpedge_label = np.ones((sharp_surface.shape[0], 1))
        sharp_surface = np.concatenate((sharp_surface, sharpedge_label), axis=1)
    rng = np.random.default_rng()
    ind = rng.choice(surface.shape[0], num_points, replace=False)
    surface = torch.FloatTensor(surface[ind])
    ind = rng.choice(sharp_surface.shape[0], num_sharp_points, replace=False)
    sharp_surface = torch.FloatTensor(sharp_surface[ind])

    return torch.cat([surface, sharp_surface], dim=0).unsqueeze(0), mesh_full


class SurfaceLoader:
    def __init__(self, num_points=8192):
        self.num_points = num_points

    def __call__(self, mesh_or_mesh_path, num_points=None):
        if num_points is None:
            num_points = self.num_points

        mesh = mesh_or_mesh_path
        if isinstance(mesh, str):
            mesh = trimesh.load(mesh, force="mesh", merge_primitives=True)
        if isinstance(mesh, trimesh.scene.Scene):
            for idx, obj in enumerate(mesh.geometry.values()):
                if idx == 0:
                    temp_mesh = obj
                else:
                    temp_mesh = temp_mesh + obj
            mesh = temp_mesh
        surface, mesh = load_surface(mesh, num_points=num_points)
        return surface


class SharpEdgeSurfaceLoader:
    def __init__(self, num_uniform_points=8192, num_sharp_points=8192, **kwargs):
        self.num_uniform_points = num_uniform_points
        self.num_sharp_points = num_sharp_points
        self.num_points = num_uniform_points + num_sharp_points

    def __call__(self, mesh_or_mesh_path, num_uniform_points=None, num_sharp_points=None):
        if num_uniform_points is None:
            num_uniform_points = self.num_uniform_points
        if num_sharp_points is None:
            num_sharp_points = self.num_sharp_points

        mesh = mesh_or_mesh_path
        if isinstance(mesh, str):
            mesh = trimesh.load(mesh, force="mesh", merge_primitives=True)
        if isinstance(mesh, trimesh.scene.Scene):
            for idx, obj in enumerate(mesh.geometry.values()):
                if idx == 0:
                    temp_mesh = obj
                else:
                    temp_mesh = temp_mesh + obj
            mesh = temp_mesh
        surface, mesh = load_surface_sharpegde(mesh, num_points=num_uniform_points, num_sharp_points=num_sharp_points)
        return surface