Frequent problems with point cloud processing

When processing point clouds, underpowered hardware can lead to several problems that can significantly impact user experience and processing efficiency. Here are some of the most common problems that can occur:

1. Slow processing speed

   Hardware that is too weak, especially a slow processor or insufficient memory, can drastically slow down the speed of point cloud processing. Importing, registering and editing point clouds takes considerably longer.

2. Programme crashes and instability

   If the hardware is not powerful enough, the programme may crash or hang frequently, especially with large or complex point clouds. Problems that we have encountered are either insufficient RAM or a graphics card that is not powerful enough.

3. Limited visualisation quality

   A weak graphics card can lead to poor rendering quality and a slow frame rate, which makes navigating and editing the point clouds more difficult. This can be noticeable in the form of stuttering, delayed display or low resolution.

4. Limited editing functions

   Certain functions, such as creating meshes or complex point cloud registration, may not be able to be performed due to hardware limitations. Some functions may be disabled or significantly slowed down.

5. Point cloud size limitation

   With weak hardware, the maximum size of point clouds that can be processed may be limited. Large point clouds may not be fully loaded, making analysis and processing difficult or impossible.

6. Overheating and hardware stress

   Hardware that is too weak and is operated at its performance limits can overheat or cause other hardware problems that shorten the service life of the components.

7. Avoid bottlenecks at all costs

   The term bottleneck is used to describe a scenario in which one component of a system limits the overall performance because it operates slower than other components. In the context of the processor (CPU) and graphics card (GPU), this refers to the situation in which either the CPU or the GPU limits the overall performance of the system. We differentiate between CPU and GPU bottlenecks.
   

   CPU bottleneck

   A CPU bottleneck occurs when the processor is not fast enough to provide the graphics card with the necessary data or instructions. This means that the CPU slows down the GPU so that the GPU cannot utilise its full potential.
   

   GPU bottleneck

   A GPU bottleneck occurs when the GPU is not powerful enough to process the data provided by the CPU fast enough. This results in the GPU limiting the overall performance of the system.

To avoid these problems, it is important to ensure that the hardware meets or exceeds the recommended system requirements, especially with regard to CPU, RAM, GPU and storage space. Get in touch with us. We will be happy to provide you with a customised offer with coordinated hardware components for your projects.