Vinod Chugani has published a four-stage study plan for computer vision engineers at KDnuggets. Each stage builds on the one before it, and each ends with a portfolio exercise.
Stage one is the math: matrix transformations, coordinate frames, projective geometry, and multi-view geometry, plus PyTorch tensor work and augmentation with Albumentations. Stage two covers Vision Transformers and Swin Transformers, then vision-language models like CLIP and SigLIP, open-vocabulary detection with OWL-ViT and Grounding DINO, and promptable segmentation with Meta’s SAM 2.
Stage three moves to 3D: point clouds, voxel grids, signed distance fields, monocular depth with Depth Anything v2, NeRFs and 3D Gaussian Splatting, and visual odometry and SLAM. Stage four is deployment, covering model compression and runtimes including TensorRT, ONNX Runtime, and OpenVINO, with hardware targets such as NVIDIA Jetson, Apple Silicon’s Neural Engine, and the Raspberry Pi AI Kit. Multi-camera streaming at 30+ FPS gets its own treatment through NVIDIA DeepStream. One suggested exercise: export YOLOv8 or RT-DETR to ONNX, quantize with TensorRT, and benchmark FP32 against INT8.
See more at kdnuggets.com and content on GitHub.
https://blog.adafruit.com/2026/10/27/the-roadmap-to-becoming-a-computer-vision-engineer-in-2026/
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