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Iterates over video frames sequentially, applying a depth estimation model to each frame and saving the resulting depth visualizations.
Distinct from Video Frame Processing: Distinct from Video Frame Processing: specifically applies depth estimation to each frame, not general frame decoding or adjustments.
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Depth-Anything is a monocular depth estimation foundation model that produces dense per-pixel depth maps from a single RGB image. It is built on a DINOv2 Vision Transformer encoder backbone and trained on 62 million unlabeled images using a teacher-student pseudo-labeling framework, enabling robust generalization across diverse scenes without task-specific training. The model outputs both relative depth maps, which capture the ordering of scene points, and metric depth maps with real-world units after fine-tuning on datasets like NYUv2 or KITTI. The project distinguishes itself through its ab
Processes video frames sequentially to generate consistent depth maps for each frame in a clip.