Anari AI

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MOST COMMON CHALLENGES IN 3D POINT CLOUD PROCESSING ⬇️

While exploring how geospatial companies process 3D point clouds, we found out that the most common obstacles come from the following challenges:

➡️ The first one is point cloud acquisition which would accurately mirror a 3D object

➡️ Visualizing the recorded system and creation of a semantic representation appears as a second problem.

Although many software approaches it, there is still a huge bottleneck when it comes to the classification of objects from the 3D point cloud datasets. This is very evident in industries that work with large point cloud datasets, such as 3D GIS, 3D CAD and BIM, which count up to tens of billions points collected in a single scan, translating to hundreds of GB or even TB of data

➡️Finally, implementing the domain knowledge in order to create a precise and reliable 3D model

💡Anari’s approach to these problems comes as a powerful combination of hardware acceleration, domain-specific algorithms, and state-of-the-art ML models. We developed Thor X to solve some of the biggest challenges the geospatial industry is facing.

✅The main output is a 30x better performance in comparison to one of the Nvdia’s best GPUs on the market, where the efficiency reflects in the throughput, price and power consumption.

Find more information here: https://anari.ai/thor-x/

The key players for 3D point cloud processing software on the global market use different approaches to tackle exacting challenges in creating a digital 3D model of a complex object in the physical world.

These challenges can be divided in three main categories:
➡️ Point cloud acquisition which would accurately mirror a 3D object
➡️ Visualizing the recorded system and creation of semantic representation
➡️ Implementing the domain knowledge in order to create precise and reliable 3D model
Regardless of many advanced software processing features, there is still a need for a huge manpower working on point cloud labeling.

👉 In order to help make these processes much more efficient, Anari AI developed a new cloud-based technology - “System-on-Cloud”, which introduces an optimized and efficient system from various hardware and software architectures combined with machine learning models.

🚀 Anari's Thor X delivers up to 30x more efficient processing compared to the best general purpose hardware available on the market, also providing many other advanced solutions that answer the mentioned problems.

✅ Read more about challenges in existing software processing of the 3D point cloud and find out how Thor X improves the process:
https://anari.ai/thor-x/