# Depth Estimation

Video Depth Anything is a deep learning-based video depth estimation model capable of providing high-quality and temporally consistent depth estimates for super-long videos. This technology is developed based on Depth Anything V2, boasting strong generalization capabilities and stability. Its primary advantages include the ability to estimate depth for any length of video, temporal consistency, and adaptability to open-world video. The model was developed by ByteDance's research team to address challenges in depth estimation for long videos, such as temporal consistency and adaptability in complex scenes. The code and demos for this model are currently available for researchers and developers.

StereoCrafter is an innovative framework that employs foundational models as priors, utilizing depth estimation and stereo video reconstruction techniques to transform 2D videos into immersive stereo 3D experiences. This technology overcomes the limitations of traditional methods, enhancing the high-fidelity generation performance required by display devices. Key advantages of StereoCrafter include its ability to process video inputs of varying lengths and resolutions, as well as optimizing video processing through autoregressive strategies and chunk processing. Additionally, StereoCrafter has developed complex data processing workflows to reconstruct large-scale, high-quality datasets that support the training process. This framework provides practical solutions for creating immersive content for 3D devices such as Apple Vision Pro and 3D monitors, potentially revolutionizing how we experience digital media.

MegaSaM is a system that allows for accurate, rapid, and robust estimation of camera parameters and depth maps from monocular videos of dynamic scenes. This system overcomes the limitations of traditional structure-from-motion and monocular SLAM techniques, which typically assume that the input videos primarily contain static scenes with significant parallax. MegaSaM can be extended to videos of complex dynamic scenes in the real world, including those with unknown fields of view and unconstrained camera paths, through carefully modified depth-visual SLAM frameworks. Extensive experiments on both synthetic and real videos demonstrate that MegaSaM is more accurate and robust in camera pose and depth estimation while being faster or comparable in runtime to previous and concurrent work.

Prompt Depth Anything is a method for high-resolution and high-precision depth estimation. This method unlocks the potential of depth foundational models through prompting techniques, using iPhone LiDAR as a cue to guide the model in generating precise depth measurements of up to 4K resolution. Additionally, it introduces a scalable data pipeline for training and has released a more detailed ScanNet++ dataset with depth annotations. The main advantages of this technology include high-resolution and high-precision depth estimation, along with benefits for downstream applications such as 3D reconstruction and generalized robotic grasping.

Depth Pro is a research project for monocular depth estimation that can rapidly generate high-precision depth maps. This model utilizes multi-scale visual transformers for dense predictions and trains on both real and synthetic datasets to achieve high accuracy and detail capture. It generates a 2.25 million pixel depth map on standard GPUs in just 0.3 seconds, making it fast and precise, highly significant for fields such as machine vision and augmented reality.

Depth Anything V2 is an improved monocular depth estimation model. Trained using synthetic images and a large amount of unlabeled real images, it provides more refined and robust depth predictions compared to the previous version. The model demonstrates significant improvements in both efficiency and accuracy, with a speed that is over 10 times faster than the latest Stable Diffusion-based models.

Control-LoRA utilizes low-rank parameter optimization added to ControlNet, providing a more efficient and compact model control method for consumer-grade GPUs. This product includes multiple Control-LoRA models, featuring capabilities such as MiDaS and ClipDrop depth estimation, Canny edge detection, photo and sketch coloring, and Revision. The Control-LoRA models are trained to generate high-quality images across diverse image concepts and aspect ratios.

Dpt Depth is an image processing tool based on Dpt depth estimation and 3D technology. It can quickly estimate depth information from input images and generate corresponding 3D models based on the depth information. Dpt Depth Estimation + 3D is powerful and easy to use, and can be widely used in computer vision, image processing and other fields. The product offers a free trial version and a paid subscription version.