eGoRG: GPU-accelerated depth estimation for immersive video applications based on graph cuts

Immersive video is gaining relevance across various fields, but its integration into real applications remains limited due to the technical challenges of depth estimation. Generating accurate depth maps is essential for 3D rendering, yet high-quality algorithms can require hundreds of seconds to produce a single frame. While real-time depth estimation solutions exist — particularly monocular deep learning-based methods and active sensors such as time-of-flight or plenoptic cameras — their depth accuracy and multiview consistency are often insufficient for depth image-based rendering (DIBR) and immersive video applications. This highlights the persistent challenge of jointly achieving real-time performance and high-quality, correlated depth across views. This paper introduces eGoRG, a GPU-accelerated depth estimation algorithm based on MPEG DERS, which employs graph cuts to achieve high-quality results. eGoRG contributes a novel GPU-based graph cuts stage, integrating block-based push-relabel acceleration and a simplified alpha expansion method. These optimizations deliver quality comparable to leading graph-cut approaches while greatly improving speed. Evaluation on an MPEG multiview dataset and a static NeRF dataset demonstrates the algorithm’s effectiveness across different scenarios. • The proposal is a novel GPU-accelerated depth estimation algorithm based on graph cuts. • Algorithm-dependent strategies are introduced to maximize the quality–time trade-off. • Depth results are comparable to high-performing graph-cut approaches while being substantially faster. • The method is training-free and can process dynamic scenes. • The algorithm is a good trade-off between quality and processing time achieving near real-time results.