All Products
Search
Document Center

Intelligent Media Management:Point cloud compression

Last Updated:Sep 22, 2026

Common methods for point cloud compression include sampling, geometric shape fitting, and the octree method. This topic describes how to compress point cloud data using the octree method.

Overview

A point cloud is a collection of many points. Storing point clouds consumes a large amount of memory and is not ideal for transmission. The available bandwidth is often insufficient to directly transmit uncompressed point clouds at the network layer. Therefore, point clouds must be compressed. The point cloud compression feature of Intelligent Media Management (IMM) analyzes and processes the spatiotemporal information of point cloud data. This significantly reduces the data volume and storage costs, providing a high-quality, real-time encoding and decoding solution for point cloud data.

Scenarios

Point cloud compression can be used in the following scenarios.

Scenario

Description

Autonomous driving

Compresses point cloud data generated by radar scans in autonomous driving scenarios.

Digital cultural heritage

Provides a compression solution for point cloud information of cultural relics in digital heritage scenarios.

Smart city

Effectively compresses point cloud information from 3D city reconstruction to enable smooth data rendering and display.

Mixed reality

Provides real-time encoding and decoding capabilities for point cloud data in mixed reality scenarios.

Prerequisites

  • You have created an AccessKey pair. For more information, see Create an AccessKey pair.

  • You have activated OSS, created a bucket, and uploaded files to the bucket. For more information, see Upload files.

  • You have activated IMM. For more information, see Activate IMM.

  • You have created a project in the IMM console. For more information, see Create a project.

    Note
    • You can also call the CreateProject operation to create a project. For more information, see Create a project.

    • You can call the ListProjects operation to list all projects in a specified region.

Procedure

Call the CreateCompressPointCloudTask operation to compress point cloud data.

Important

Task information is retained for only seven days. After this period, the information cannot be retrieved. To obtain task information promptly, use one of the following methods:

Compression information

  • Project name: test-project

  • OSS URI of the point cloud file: oss://test-bucket/test-object.pcd

  • Compression algorithm: Octree

  • OSS URI of the output file: oss://test-bucket/test-target-object

Compression algorithm overview

Octree is a common compression algorithm that is mainly used for the compression and representation of 3D data. It divides a 3D space into a series of equally sized cubes. Each cube is divided into eight sub-cubes, which can be further divided into smaller sub-cubes, and so on.

By storing only the nodes that contain valid data, the octree method can significantly reduce storage space. It is often used for the compression and representation of 3D data, such as point cloud data and volumetric data. By selecting appropriate division and merging strategies, you can reduce storage and transmission overhead while maintaining data precision.

Request example

{
    "ProjectName": "test-project",
    "SourceURI": "oss://test-bucket/test-object.pcd",
    "UserData": "{\"ID\":\"testuid\",\"Name\": \"test-user\",\"Avatar\": \"http://test.com/testuid\"}",
    "TargetURI": "oss://test-bucket/test-target-object",
    "PointCloudFileFormat": "pcd",
    "CompressMethod": "octree",
    "PointCloudFields": "[\"xyz\"]",
    "OctreeOption": "{\"PointResolution\": 0.001, \"OctreeResolution\": 0.01, \"DoVoxelGridDownDownSampling\": false, \"LibraryName\": \"pcl\"}",
    "KdtreeOption": "{\"CompressionLevel\": 8, \"QuantizationBits\": 10, \"LibraryName\": \"draco\"}"
}

Response example

{
    "TaskId": "PointCloudCompress-091d9b4a-8726-47bf-b699-d24c7daff63c",
    "RequestId": "8B0EEA2E-35FE-500F-BCDB-E2E7CA11DF7A",
    "EventId": "180-1S7Q8gHbVXJf2lekgesKvlNM1VR"
}

Sample code

The following sample code shows how to compress a point cloud using the Python SDK.

# -*- coding: utf-8 -*-
# This file is auto-generated, don't edit it. Thanks.
import os
import sys

from typing import List

from alibabacloud_imm20200930.client import Client as imm20200930Client
from alibabacloud_tea_openapi import models as open_api_models
from alibabacloud_imm20200930 import models as imm_20200930_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient


class Sample:
    def __init__(self):
        pass

    @staticmethod
    def create_client(
        access_key_id: str,
        access_key_secret: str,
    ) -> imm20200930Client:
        """
        Initializes a client using an AccessKey ID and an AccessKey secret.
        @param access_key_id:
        @param access_key_secret:
        @return: Client
        @throws Exception
        """
        config = open_api_models.Config(
            access_key_id=access_key_id,
            access_key_secret=access_key_secret
        )
        # Specify the endpoint of IMM.
        config.endpoint = f'imm.cn-beijing.aliyuncs.com'
        return imm20200930Client(config)

    @staticmethod
    def main(
        args: List[str],
    ) -> None:
        # An Alibaba Cloud account AccessKey has permissions to call all API operations. We recommend that you use a Resource Access Management (RAM) user to call API operations or perform routine O&M.
        # We strongly recommend that you do not save your AccessKey ID and AccessKey secret in your project code. This may cause an AccessKey leak and threaten the security of all resources in your account.
        # This example shows how to obtain an AccessKey from an environment variable for identity verification when you call an API operation.
        imm_access_key_id = os.getenv("AccessKeyId")
        imm_access_key_secret = os.getenv("AccessKeySecret")
        client = Sample.create_client(imm_access_key_id, imm_access_key_secret)
        octree_option = imm_20200930_models.OctreeOption(
            point_resolution=0.001,
            octree_resolution=0.01,
            do_voxel_grid_down_down_sampling=False,
            library_name='pcl'
        )
        create_compress_point_cloud_task_request = imm_20200930_models.CreateCompressPointCloudTaskRequest(
            source_uri='oss://test-bucket/test-object.pcd',
            target_uri='oss://test-bucket/test-target-object',
            point_cloud_file_format='pcd',
            compress_method='octree',
            point_cloud_fields=[
                '[\"xyz\"]'
            ],
            project_name='test-project',
            octree_option=octree_option
        )
        runtime = util_models.RuntimeOptions()
        try:
            # If you copy the code to run it, print the return value of the API operation.
            client.create_compress_point_cloud_task_with_options(create_compress_point_cloud_task_request, runtime)
        except Exception as error:
            # If an error occurs, print the error message.
            UtilClient.assert_as_string(error.message)

    @staticmethod
    async def main_async(
        args: List[str],
    ) -> None:
        # An Alibaba Cloud account AccessKey has permissions to call all API operations. We recommend that you use a RAM user to call API operations or perform routine O&M.
        # We strongly recommend that you do not save your AccessKey ID and AccessKey secret in your project code. This may cause an AccessKey leak and threaten the security of all resources in your account.
        # This example shows how to obtain an AccessKey from an environment variable for identity verification when you call an API operation.
        imm_access_key_id = os.getenv("AccessKeyId")
        imm_access_key_secret = os.getenv("AccessKeySecret")
        client = Sample.create_client(imm_access_key_id, imm_access_key_secret)
        octree_option = imm_20200930_models.OctreeOption(
            point_resolution=0.001,
            octree_resolution=0.01,
            do_voxel_grid_down_down_sampling=False,
            library_name='pcl'
        )
        create_compress_point_cloud_task_request = imm_20200930_models.CreateCompressPointCloudTaskRequest(
            source_uri='oss://test-bucket/test-object.pcd',
            target_uri='oss://test-bucket/test-target-object',
            point_cloud_file_format='pcd',
            compress_method='octree',
            point_cloud_fields=[
                '[\"xyz\"]'
            ],
            project_name='test-project',
            octree_option=octree_option
        )
        runtime = util_models.RuntimeOptions()
        try:
            # If you copy the code to run it, print the return value of the API operation.
            await client.create_compress_point_cloud_task_with_options_async(create_compress_point_cloud_task_request, runtime)
        except Exception as error:
            # If an error occurs, print the error message.
            UtilClient.assert_as_string(error.message)


if __name__ == '__main__':
    Sample.main(sys.argv[1:])

Billing

During point cloud compression, the following billable items are generated for OSS and IMM:

  • For OSS: For detailed pricing, see OSS Pricing.

    API

    Billable item

    Description

    GetObject

    GET requests

    Fees are calculated based on the number of successful requests.

    Outbound traffic over the internet

    If you call the GetObject operation using a public endpoint (for example, oss-cn-hangzhou.aliyuncs.com) or an acceleration endpoint (for example, oss-accelerate.aliyuncs.com), fees are charged for outbound traffic over the internet. These fees are based on the data volume.

    Volume of retrieved Infrequent Access data

    If the retrieved data is Infrequent Access data, fees are charged for the volume of retrieved Infrequent Access data. These fees are based on the volume of retrieved data.

    Volume of data retrieved using real-time access of Archive objects

    If you read an Archive object from a bucket for which real-time access of Archive objects is enabled, fees are charged for the volume of data retrieved. These fees are based on the volume of retrieved data.

    Transfer acceleration

    If you enable transfer acceleration and use an acceleration endpoint to access your bucket, fees are charged for transfer acceleration. These fees are based on the data volume.

  • For IMM: For detailed pricing, see IMM billable items.

    Important

    Starting from 11:00 on July 28, 2025 (UTC+8), the point cloud compression feature of IMM will become a paid service. For more information, see IMM Billing Adjustment Announcement.

    API

    Billable item

    Description

    CreateCompressPointCloudTask

    PointCloudCompress

    Fees are calculated based on the number of successful requests.