#depth estimation

Explore Monodepth2's PyTorch-based reference implementation for advanced depth estimation. The project methodically outlines dependency setups, guides on predicting scaled disparities, and furnishes ready-to-use pretrained models. Detailed instructions for custom dataset training, model finetuning, and KITTI dataset evaluation are included, alongside support for multiple training modalities including monocular, stereo, and combined approaches, catering to diverse research requirements.

UniDepth provides universal metric depth estimation from single images, accommodating adaptable input shapes, confident predictions, and swift processing. This leading model in depth estimation boasts a strong decoder design and ONNX compatibility, excelling in tests like KITTI and NYUv2. The project progresses with compact model versions and continual image processing enhancements. It facilitates seamless model access via Hugging Face and TorchHub, fostering integration in various AI frameworks. Researchers are encouraged to contribute to this innovative tool, advancing automated visual comprehension.

SC-Depth offers a robust, three-version framework for self-supervised monocular depth estimation. V1 features geometry consistency and self-discovered masks for reliable depth predictions. V2 enhances indoor scene accuracy with an auto-rectify network managing rotation challenges. V3 uses pretrained networks for improved depth accuracy in dynamic scenes. With support for multiple datasets and comprehensive scripts, it suits diverse research and application needs.

Depth Anything V2 presents a refined depth estimation model offering improved detail accuracy and robustness compared to its predecessor. It outperforms SD-based models in speed, parameter efficiency, and precision. With extensive integration into Apple Core ML and Transformers and new pre-trained models, it provides broad usability. The release supports easy setup for image and video depth mapping through code or demo apps on platforms like Hugging Face. The open-source model is available under the Apache-2.0 license for smaller versions and CC-BY-NC-4.0 for others, promoting community engagement and accessibility.

This implementation uses the 3D Ken Burns Effect to animate static images with PyTorch, incorporating virtual camera pans and zooms. It utilizes CuPy for efficient CUDA processing and offers both automated and manual camera path adjustments. The tool can be run locally or on Colab with community notebooks, and includes depth estimation features for refined animations. Benchmarking scripts are provided for performance verification. Licensed for non-commercial use under Creative Commons, this tool provides an innovative approach to adding dynamic depth and motion to images.