Firefly's AIBOX embedded PC for the NVIDIA Jetson Orin series can achieve depth estimation. This technology predicts scene depth information from a single or multiple images. It is widely used in the field of computer vision models, especially in tasks such as 3D reconstruction, scene understanding, and environmental perception.
Depth estimation technology can be divided into multi-camera depth estimation and monocular depth estimation. Monocular cameras have the advantages of low cost, widespread equipment, and convenient image acquisition, making monocular depth estimation technology a hot topic. The rapid development of deep learning technology has also provided new solutions for monocular depth estimation.
Hardware Support
The AIBOX embedded computer with NVIDIA core modules can achieve monocular depth estimation. All products in this series are equipped with NVIDIA Jetson Orin system on modules, which provide excellent AI computing power, large-capacity unified memory, and a comprehensive software stack to drive the latest generative AI applications with ultra-high energy efficiency.
| AIBOX-OrinNX | AIBOX-OrinNano | |
| Model | NVIDIA Jetson Orin NX 16GB | NVIDIA Jetson Orin Nano 8GB |
| AI Performance | 157 TOPS | 67 TOPS |
| GPU | 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores | 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores |
| CPU | 8-core Arm Cortex-A78 64-bit CPU 2MB L2 + 4MB L3 | 6-core Arm Cortex A78 64-bit CPU1.5MB L2 + 4MB L3 |
| DDR | 16GB 128-bit LPDDR5 102.4GB/s | 8GB 128-bit LPDDR5 68 GB/s |
| HDMI | 4K@60Hz | 4K@30Hz |
What are monocular depth estimation algorithms?
Monocular depth estimation refers to estimating the depth information of objects in a scene through images obtained by a single camera. Compared with multi-camera depth estimation methods, monocular depth estimation methods only use a single camera and rely only on the visual information of the image to perform depth inference for each pixel.
FastDepth
FastDepth is a depth estimation algorithm for embedded systems. Its core features are its good balance between speed and accuracy and its ability to run on resource-limited devices such as smartphones.
In order to reduce the model size, improve computing efficiency, and make the model more suitable for embedded devices, the NetAdapt algorithm was used to trim FastDepth.
Download source code
- $ git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference
Compile/Install
Example of running
- $ ./depthnet.py room_0.jpg output_room_0.jpg
Application Scenario
Autonomous driving
- In self-driving cars, monocular depth estimation technology is used to provide real-time, low-cost depth information to help vehicles identify obstacles, etc., so as to make correct driving decisions.
Robot navigation
- In environments such as factories, warehouses, and homes, robots require accurate depth information to identify obstacles and avoid them. Monocular depth estimation technology can provide robots with real-time depth perception capabilities and improve the accuracy of their navigation and operation.
Augmented reality (AR)
- In augmented reality applications, monocular depth estimation technology provides accurate depth information, allowing virtual content to be more naturally integrated into real scenes and improving user experience.
Security monitoring
- In the field of security monitoring, monocular depth estimation technology helps the monitoring system identify the distance and speed of moving objects, improving the accuracy and real-time performance of monitoring.
Virtual Reality (VR) and Gaming
- In virtual reality and games, monocular depth estimation technology provides users with a more realistic immersive experience. With precise depth information, objects in games and virtual reality scenes can present a more realistic perspective effect.
