Function transform_mesh

data_toolkit/utils.py:496–539

Apply multi-view transform to mesh vertices based on camera transform matrix.

(mesh_v, frame)

def transform_mesh(mesh_v, frame):
    """
    Apply multi-view transform to mesh vertices based on camera transform matrix.
    """
    device = mesh_v.device
    c2w_orig = torch.tensor(frame['transform_matrix'], dtype=torch.float32, device=device)

    # Old and new camera matrices
    radius = c2w_orig[:3, 3].norm().item()
    c2w_new = get_new_camera_matrix(radius=radius, yaw=-90/180.0*math.pi, pitch=0.0,
                                dtype=torch.float32, device=device)
    w2c_orig = torch.inverse(c2w_orig)

    # Initial and final axis alignment matrices
    R_init = torch.tensor([
        [1.0, 0.0,  0.0, 0.0],
        [0.0, 0.0, -1.0, 0.0],
        [0.0, 1.0,  0.0, 0.0],
        [0.0, 0.0,  0.0, 1.0]
    ], dtype=torch.float32, device=device)

    R_back = torch.tensor([
        [1.0,  0.0, 0.0, 0.0],
        [0.0,  0.0, 1.0, 0.0],
        [0.0, -1.0, 0.0, 0.0],
        [0.0,  0.0, 0.0, 1.0]
    ], dtype=torch.float32, device=device)

    R_ply = torch.tensor([
        [1.0,  0.0, 0.0, 0.0],
        [0.0,  0.0, 1.0, 0.0],
        [0.0, -1.0, 0.0, 0.0],
        [0.0,  0.0, 0.0, 1.0]
    ], dtype=torch.float32, device=device)

    T_cam = c2w_new @ w2c_orig @ R_ply
    T_final = R_back @ T_cam @ R_init

    # Apply transform
    mesh_v = mesh_v.reshape(-1, 3)
    verts_h = torch.cat([mesh_v, torch.ones((mesh_v.shape[0], 1), dtype=torch.float32, device=device)], dim=1)
    verts_trans = (T_final @ verts_h.T).T[:, :3]

    return verts_trans


def sphere_normalize_torch(vertices):