# DPT architecture

A fine-tuned version of Depth Anything V2 for outdoor metric depth estimation tasks, trained on the synthetic dataset Virtual KITTI

Depth Anything V2 is a depth estimation model based on the DPT architecture, utilizing a DINOv2 backbone network. It achieves fine and robust depth prediction through training on large-scale synthetic and real-world data.

Depth Anything is a depth estimation model based on the DPT architecture and DINOv2 backbone network, trained on approximately 62 million images, achieving state-of-the-art results in relative and absolute depth estimation tasks.

ZoeDepth is a model for monocular depth estimation, specifically fine-tuned on the NYU dataset, capable of zero-shot transfer and metric depth estimation.

Depth Anything is a depth estimation model based on the DPT architecture and DINOv2 backbone network, trained on approximately 62 million images, achieving state-of-the-art results in both relative and absolute depth estimation tasks.

Depth Anything is a depth estimation model based on the DPT architecture and DINOv2 backbone network, trained on approximately 62 million images, achieving state-of-the-art performance in zero-shot depth estimation.

Depth Anything is a depth estimation model based on the DPT architecture, utilizing the DINOv2 backbone network. It was trained on approximately 62 million images and excels in both relative and absolute depth estimation tasks.