FlashDepth: Real-time Streaming Depth Estimation at 2K Resolution

Gene Chou Cornell

Wenqi Xian Netflix

Guandao Yang Stanford

Mohamed Abdelfattah Cornell

Bharath Hariharan Cornell

Noah Snavely Cornell

Ning Yu Netflix

Paul Debevec Netflix

arxiv: 2504.07093

Abstract

A versatile video depth estimation model should (1) be accurate and consistent across frames, (2) produce high-resolution depth maps, and (3) support real-time streaming. We propose FlashDepth, a method that satisfies all three requirements, performing depth estimation on a 2044x1148 streaming video at 24 FPS. We show that, with careful modifications to pretrained single-image depth models, these capabilities are enabled with relatively little data and training. We evaluate our approach across multiple unseen datasets against state-of-the-art depth models, and find that ours outperforms them in terms of boundary sharpness and speed by a significant margin, while maintaining competitive accuracy. We hope our model will enable various applications that require high-resolution depth, such as video editing, and online decision-making, such as robotics. We release all code and model weights at https://github.com/Eyeline-Research/FlashDepth