主函数 - 数据并行推理

Normalize mesh vertices to sphere

将点云数据按维度独立归一化到[-1, 1]范围。 参数: points (np.ndarray): 输入点云，形状为(n, 3)。 返回: np.ndarray: 归一化后的点云。 tuple: 每

Normalizes the scene by scaling and translating it to fit in a unit cube centered at the origin. Mostly taken from the Point-E / Shap-E rende

Normalize the vertices V to fit inside a unit bounding box [0,1]^3. V: (n,3) numpy array of vertex positions. Returns: normalized V

normalize mesh to fit in unit sphere scene_mesh: trimesh mesh

解析命令行参数

Standard multivariate normal prior Assume z is batched

Return node and worker info for PyTorch and some distributed environments.

Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the

Abstract method to extract surface mesh from grid logits. This method should be implemented by subclasses. Raises:

Sample from the diagonal Gaussian distribution. Returns: torch.Tensor: A sample tensor with the same shape as the mean.

returns a sampling function with given ODE settings Args: - sampling_method: type of sampler used in solving the ODE; default to be Do

images: [B, 3, H, W]

points: [B, N, 7]

初始化分布式环境（仅用于数据并行）

设置日志

Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be

主函数:设置场景并处理所有材质

Expand tar files. Args: data: iterator over opened tar file streams. handler: exception handler. select_files: select fil

Loss for training the score model Args: - model: backbone model; could be score, noise, or velocity - x1: datapoint -

Triangulates all meshes in the scene. Returns: None

Unhides all objects in the scene. Returns: None

Open URLs and yield a stream of url+stream pairs. Args: data: iterator over dict(url=...) handler: exception handler. kw:

Decorator: wrap the function to time its execution.

Process a batch of grid logits to extract surface meshes. Args: grid_logits (torch.Tensor): Batch of grid logits with sh

Context manager entry: start timing.

Context manager exit: stop timing and log results.