Since Meta gave access to the passthrough camera for Meta Quest 3, I explored its capabilities in MR by developing several experimental modules, including QR-integrated automated crawlers and real-time cartoonification of passthrough frames. These experiments demonstrated some noticeable insights about the processing performance for advancing MR applications:
- On-device processing is notably faster than remote inference, even when the remote server is local.
- Detection accuracy is heavily influenced not by the model architecture alone, but by the quality (resolution/frame integrity) of passthrough input, which can degrade real-time performance. Sometimes, perceptual clarity is reduced by a couple of taxels per feature.
Therefore, the evaluation criteria of those applications might also be slightly different, while not relying solely on accuracy or task performance.
Fortunately, developing the application for a dynamic environment is quite easy.
Performance Insights for Real-Time MR Processing on Meta Quest 3
The Meta Quest 3 boasts significantly improved processing power compared to its predecessors, which directly impacts its ability to handle real-time MR tasks. Here's a breakdown of potential performance insights:
- Latency: This is a critical factor in MR experiences. Real-time processing demands low latency between the physical world events captured by the passthrough cameras, the digital augmentation, and the rendering to the user's eyes. The Quest 3's enhanced Snapdragon XR2 Gen 2 platform aims to reduce this latency, leading to a more seamless and believable MR experience. However, the complexity of the processing pipeline (image processing, scene understanding, rendering, etc.) will directly influence the overall latency. Expect to see metrics like end-to-end latency measured in milliseconds. For comfortable interaction, target latencies below 20ms are generally desired.
- Frame Rate Stability: Maintaining a consistent and high frame rate (ideally 72Hz, 90Hz, or even 120Hz) is crucial for visual comfort and reducing motion sickness. Real-time MR applications that involve complex scene analysis, intricate 3D overlays, or dynamic interactions will put a strain on the GPU. Performance testing will likely involve monitoring frame rate stability under various processing loads to identify bottlenecks.
- Computational Throughput: The sheer amount of data being processed in real-time MR is substantial. This includes video streams from multiple cameras, sensor data, and the computations required for scene understanding (e.g., plane detection, object recognition, spatial mapping), tracking, and rendering. Evaluating the computational throughput of the Quest 3's CPU and GPU will be essential to understand the limits of what can be achieved in real-time. Metrics like frames per second for specific processing tasks, the number of features tracked, or the complexity of the virtual content rendered simultaneously will be important.
- Power Consumption and Thermal Management: Intensive real-time processing will inevitably impact power consumption and heat generation. Longer and more demanding MR sessions will require careful optimization to manage battery life and prevent overheating, which could lead to performance throttling or user discomfort. Monitoring power draw and surface temperatures under sustained processing loads will be crucial.
These are really great.
Can you please provide some literature that used such criteria of evaluation so that I can get some ideas.
Thank you.
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