computer-vision-expert

Building a real-time computer vision pipeline for edge devices involves selecting models like YOLO26 and SAM 3, then applying quantization and hardware-specific exports such as ONNX or TensorRT. This process optimizes latency and accuracy for mobile, browser, and embedded deployment targets.

The best way to integrate VLMs with 3D reconstruction in AR is by using calibration pipelines for depth and spatial reasoning. This enables real-time object detection and depth-aware rendering, allowing VLMs to provide contextual understanding within AR environments on mobile or browser platforms.

YOLO26 works with SAM 3 for real-time segmentation on mobile platforms by combining YOLO26's detection with SAM 3's segmentation. The pipeline is optimized through quantization and hardware-specific exports, enabling efficient real-time performance on mobile and browser deployment targets.

Quantization and ONNX or TensorRT exports are needed for computer vision deployment when targeting edge devices with strict latency constraints. These deployment optimization techniques reduce model size and inference time, enabling real-time performance across mobile, browser, and embedded platforms.

Spatial reasoning enhances depth-aware rendering in AR systems through calibration pipelines that map 3D reconstruction data. This enables real-time vision systems to accurately interpret object depth and spatial relationships, facilitating precise overlay and interaction in AR contexts on mobile and browser platforms.