Table of Contents

Mega scene updates

Incremental updates and lossless full updates are advanced features provided by EasyAR Mega to handle scene changes. They ensure the original coordinate system remains unchanged after scene updates, preventing any positional shifts in existing AR content, thereby significantly improving application development efficiency.

Supplemental update

The supplemental update feature allows developers to append and upload new video data to an already successfully constructed spatial map. The cloud algorithm will attempt to "stitch" the feature points of the new data into the coordinate system of the old map, thereby achieving map updates without the need to create a brand-new Block spatial map from scratch.

Related features:

Lossless full update

The lossless full update feature allows developers to comprehensively update the entire scene using newly collected data while maintaining the original coordinate system. The cloud algorithm reconstructs the spatial map based on the new video data, ensuring that the positions of developed AR content do not shift, achieving seamless updates.

Related features:

Feature comparison

Feature Supplemental update Lossless full update
Applicable scenario Partial area update or supplement Comprehensive update of the entire scene
Collection device GoPro Max 360 camera GoPro Max 360 camera
Data source Based on the original map, append new video data for supplementation Based on the original map, rebuild using newly collected data
Coordinate system retention Yes Yes

Frequently asked questions

  • What is the difference between incremental mapping update, lossless full re-mapping, and multi-map fusion mapping?

    Incremental mapping update: Adds new data to an existing map for updating or supplementing local areas while preserving the original coordinate system.

    Lossless full re-mapping: Comprehensively updates the entire scene by re-mapping with newly collected data while retaining the original coordinate system. The updated spatial map contains only newly submitted data, excluding original data.

    Multi-map fusion mapping: Combines multiple independently collected regional maps into a complete map, typically used for parallel collection in ultra-large scenes.

  • Can incremental mapping update be used if the entire scene is renovated?
    No, the algorithm cannot effectively match new videos with old map content. If unrenovated areas exist outside the renovated scene, collect these areas together to enable incremental updates through feature fusion.

  • Will failed incremental or lossless full re-mapping affect the original map?
    No. Regardless of success or failure of the mapping task, the original map remains unaffected. A new map dataset is generated upon successful mapping without overwriting the original.