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The paper introduces VDPP, a novel video depth post-processing framework designed to enhance the speed and accuracy of depth estimation by focusing on targeted geometric refinement in low-resolution space. VDPP avoids computationally expensive scene reconstruction, instead using dense residual learning to drive geometric representations, achieving high frame rates (>43.5 FPS on NVIDIA Jetson Orin Nano). The RGB-free architecture ensures scalability and immediate integration with any evolving image depth model, offering a practical solution for real-time edge deployment.
Achieve real-time video depth estimation on edge devices without sacrificing accuracy by ditching full scene reconstruction for targeted geometric refinement.
Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-to-end video depth models have established state-of-the-art performance. Although current end-to-end (E2E) models have achieved state-of-the-art performance, they function as tightly coupled systems that suffer from a significant adaptation lag whenever superior single-image depth estimators are released. To mitigate this issue, post-processing methods such as NVDS offer a modular plug-and-play alternative to incorporate any evolving image depth model without retraining. However, existing post-processing methods still struggle to match the efficiency and practicality of E2E systems due to limited speed, accuracy, and RGB reliance. In this work, we revitalize the role of post-processing by proposing VDPP (Video Depth Post-Processing), a framework that improves the speed and accuracy of post-processing methods for video depth estimation. By shifting the paradigm from computationally expensive scene reconstruction to targeted geometric refinement, VDPP operates purely on geometric refinements in low-resolution space. This design achieves exceptional speed (>43.5 FPS on NVIDIA Jetson Orin Nano) while matching the temporal coherence of E2E systems, with dense residual learning driving geometric representations rather than full reconstructions. Furthermore, our VDPP's RGB-free architecture ensures true scalability, enabling immediate integration with any evolving image depth model. Our results demonstrate that VDPP provides a superior balance of speed, accuracy, and memory efficiency, making it the most practical solution for real-time edge deployment. Our project page is at https://github.com/injun-baek/VDPP
