NVIDIA AIBOX 实现单目深度估计
Real-Time Monocular Depth Estimation with NVIDIA AIBOX
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The NVIDIA series AIBOX launched by Firefly enables depth estimation, a technology that predicts scene depth information from single or multiple images. It is widely used in the field of computer vision and plays a key role in tasks such as 3D reconstruction, scene understanding, and environmental perception.
Depth estimation technology can be divided into multi-view depth estimation and monocular depth estimation. Monocular cameras are particularly favored due to their low cost, widespread availability, and ease of image acquisition, making monocular depth estimation a highly focused area of research. The rapid development of deep learning has also provided new solutions for monocular depth estimation.
NVIDIA Series AIBOX
The AIBOX based on NVIDIA core modules achieves monocular depth estimation. These products are equipped with original NVIDIA Jetson Orin core modules, providing exceptional AI computing power, large-capacity unified memory, and a comprehensive software stack, capable of driving the latest generative AI applications with ultra-high energy efficiency.
| AIBOX-OrinNX | AIBOX-OrinNano | |
|---|---|---|
| Module | Jetson Orin NX 16GB | 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 CPU2MB 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 |
FastDepth
FastDepth is a depth estimation algorithm designed for embedded systems. Its core feature is the excellent balance between speed and accuracy, allowing it to run on devices with limited resources, such as smartphones or embedded systems.
Network Architecture:
To reduce model size and improve computational efficiency, making the model more suitable for embedded devices, the NetAdapt algorithm was used to prune FastDepth.
Network Pruning:
Download Source Code
$ git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inferenceBuild / Install
Reference: https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md
Run Example
$ ./depthnet.py room_0.jpg output_room_0.jpg
Application Scenarios
- Autonomous Driving: In autonomous vehicles, monocular depth estimation technology provides real-time, low-cost depth information to help vehicles identify obstacles, road markings, and traffic signals, thereby making correct driving decisions.
- Robot Navigation: In environments such as factories, warehouses, and homes, robots require precise depth information to identify obstacles and avoid collisions. Monocular depth estimation technology provides robots with real-time depth perception capabilities, improving the accuracy of their navigation and manipulation.
- Augmented Reality (AR): In AR applications, monocular depth estimation technology provides precise depth information, allowing virtual content to blend more naturally into real-world scenes and enhancing the user experience.
- Security Surveillance: In the field of security monitoring, monocular depth estimation technology helps surveillance systems identify the distance and speed of moving objects, improving the accuracy and real-time performance of monitoring.
- Virtual Reality and Gaming: In VR and gaming, monocular depth estimation technology provides users with a more realistic immersive experience. Through precise depth information, objects in games and VR scenes can present more realistic perspective effects.