المفاهيم الأساسية
Monocular depth estimation using a novel lightweight vision transformer architecture, METER, achieves state-of-the-art results on embedded devices.
الملخص
The content discusses the development of METER, a lightweight vision transformer architecture for monocular depth estimation. It addresses the limitations of active depth sensing systems and focuses on monocular depth estimation (MDE) from single RGB video frames. The proposed METER architecture aims to achieve accurate estimations and low latency inference performances on embedded hardware like NVIDIA Jetson TX1 and Jetson Nano. The paper outlines the design of METER, including three alternative configurations, a novel loss function, and a data augmentation strategy to enhance predictions. Results show that METER outperforms previous lightweight models on benchmark datasets NYU Depth v2 and KITTI.
Introduction to Depth Estimation Challenges in Computer Vision
Importance of Monocular Depth Estimation (MDE)
Development of METER Architecture for Lightweight ViT in MDE
Evaluation on Benchmark Datasets: NYU Depth v2 and KITTI
الإحصائيات
State of the art MDE models rely on vision transformers (ViT) architectures.
Researchers propose METER as a novel lightweight ViT architecture for monocular depth estimation.
METER achieves state-of-the-art estimations and low latency inference performances on embedded hardware.