Mar 8, 2019 · This paper demonstrates real-time monocular depth estimation using a deep neural network with the lowest latency and highest throughput on an embedded platform.

It is meant to be performed on a host machine with a CUDA GPU, not on an embedded platform. Deployment on an embedded device is discussed in the next section.

In this paper, we address the problem of fast depth estimation on embedded systems. We propose an efficient and lightweight encoder-decoder network architecture.

We explore learning-based monocular depth estimation, targeting real-time inference on embedded systems.

In this paper, we address the problem of fast depth estimation on embedded systems. We propose an efficient and lightweight encoder-decoder network architecture ...

Oct 30, 2019 · This project explores learning-based monocular depth estimation, targeting real-time inference on embedded systems. We propose an efficient and ...

This repo contains Pytorch implementation of depth estimation deep learning network based on the published paper: FastDepth: Fast Monocular Depth Estimation ...

Depth sensing is a critical function for robotic tasks such as localization, mapping and obstacle detection. There has been a significant and growing ...

Nov 10, 2021 · 5.7K subscribers in the JetsonNano community. A subreddit for discussing the NVIDIA Jetson Nano, TX2, Xavier NX and AGX modules and all ...

This paper proposes an efficient and lightweight encoder-decoder network architecture and applies network pruning to further reduce computational complexity ...