TOF camera module depth perception precision improvement scheme in AR

In the development of augmented reality (AR) technology, depth perception accuracy directly affects the integration effect of virtual objects with real scenes. The TOF (Time of Flight camera module, with its ability to acquire three-dimensional spatial data in real time, has become the core component of AR devices. However, how to further improve its depth perception accuracy complex environments is still the focus of the industry. This article will discuss the depth perception precision improvement scheme of TOF camera module in AR applications from three dimensions: technical optimization, design, and multi-sensor fusion.

Traditional TOF sensors are prone to interference from ambient light, in noisy depth data. The solution customized by Ouster for Meizu 17 Pro employs high-performance filtering algorithms, which, through adaptive noise suppression technology, eliminate high low-frequency noise specifically, significantly improving the clarity of the depth map. In addition, combined with the depth engine optimized by Qualcomm DSP, the system power consumption is reduced 15%, while maintaining a stable frame rate of 30FPS, ensuring the fluency of AR applications.

To compensate for the problem of insufficient To resolution, the DELTAR framework developed by the Zhejiang University team achieves lightweight ToF and RGB image fusion through deep learning. This scheme uses the texture details of the RGB to supplement ToF's depth information. In the ECCV 2022 experiment, its depth estimation error was reduced by 23% compared to traditional methods and its computational efficiency was increased by 40%, making it suitable for resource-constrained devices such as mobile terminals.

Hardware-level innovation is the foundation for precision improvement. Ouster's Femto-W module uses iToF technology to achieve millimeter- accuracy within the range of 0.2-2.5 meters, integrates a depth computing power platform, and does not require external computing power support. Its ultra-wide- design (120° field of view) can capture a broader spatial information, and the Y16 format output of infrared and depth data provides high-fidelity data for scene modeling.

For mass production needs, the module considers the efficiency of calibration on the production line during hardware selection, improves the yield through one-stop calibration technology, and complex functions such as 3D face recognition, SLAM, etc., meeting the dual needs of consumer electronics and industrial automation scenarios.

Mono ToF sensors still have limitations complex lighting or low-texture scenarios. By integrating multi-modal data such as RGB and IMU, a more complete depth perception system can be constructed. For example, the AR ruler function of Meizu 18 Pro combines ToF depth data with IMU attitude information to achieve centimeter-level distance measurement accuracy. The DELTAR framework, the feature alignment algorithm, pixel-level registration of ToF depth map and RGB image, eliminates parallax errors and enhances the spatial positioning accuracy of virtual objects.

In addition in dynamic scenes, multi-sensor fusion can effectively solve the problem of motion blur. By synchronously collecting ToF and RGB data, and combining time sequence optimization algorithm, the system real-time correct the depth deviation caused by motion, ensuring the stability of AR interaction.

At present, ToF lens modules achieved breakthrough applications in mobile phone AR. The video real-time blurring function of Meizu 17 Pro, through the ToF depth engine, realizes the precise separation the background and the subject, and the transition of the blurring is more natural; the customized solution of Orbbec for 18 Pro supports innovative functions such as AR vision, which expands the application boundary of AR in low-light environment. In the future, with the development of lightweight algorithms and low-power hardware, ToF modules will evolve smaller sizes and lower costs, promoting the popularization of AR technology in smart home, industrial inspection and other fields.

The improvement of depth perception accuracy of ToF lens module to rely on the coordinated development of algorithm optimization, hardware innovation and multi-modal fusion. Through the continuous breakthrough of technical bottlenecks, ToF will become the core driving force for devices to achieve "seamless integration of virtual and real", bringing users a more immersive and more accurate interactive experience.