Feature: Displays
Time-of-fl ight methods A time-of-fl ight camera measures distance by bouncing a beam of light off an object and back to a sensor, and measuring the round-trip time. T e process is similar to ultrasound – which uses sound to measure distance, and radar – which uses radio waves. Being light-based, time-of-fl ight cameras
generate high-resolution depth maps faster than ultrasound, and can cover greater distances. Radar may have a longer range, but time-of-fl ight is more accurate, with higher resolution. T ere are two primary methods for
measuring the time delay in time-of-fl ight: indirect time-of-fl ight (iToF) and direct time-of-fl ight (dToF). An iToF system uses the continuous-wave (CW) method, which measures the phase shiſt between the sent and received light pulses. On the other hand, a dToF camera uses the pulse-based method, which measures the elapsed time between the emitted and received light pulse.
CW iToF image sensors are mass
produced on a traditional semiconductor base, achieving high pixel densities for short-range imaging, at an attractive price- point. ToF is a useful functionality in home
devices; making apt decisions, like adjusting ambient temperature based
on the number of people in the room, or identifying shopping items based on previous buys. Which technology to select is down
to the application. iToF is better suited to short-range imaging (0.5-10m) that requires high spatial resolution; dToF is more applicable to longer-range imaging requiring lower spatial resolution. Artifi cial intelligence (AI) and optical system design are blurring the boundaries between the two technologies. In the context-aware intelligent edge systems, both iToF and dToF sensors are fused with RGB and inertial sensors with the assistance of AI for improved system performance and fewer artifacts.
Contactless 3D interaction Just like the mouse revolutionised computer interaction and touchscreen technologies drove the adoption of smartphones and tablets, time-of-fl ight will empower contactless 3D interactions. Time-of-fl ight technology is already advancing Industry 4.0, from industrial machine vision for quality inspection, to volumetric detection for asset management, and vehicle navigation in autonomous manufacturing. Industry is earnestly adopting these sensing technologies, motivating the move toward higher-resolution systems.
In our daily life, applications that use
time-of-fl ight technologies continue to emerge. T ese include in-cabin safety in vehicles, home exercise equipment, gaming and 3D lifelike remote collaborations. One future application is in autonomous driving, where ToF sensing will complement radar, LIDAR and the other depth sensors. Time-of-fl ight is also making home
theater systems smart, to dynamically adjust the sound to compensate for changes in listener location, or changes in the environment, such moved or added piece of furniture. T en, there’s online shopping, where
ToF enables measuring a space for, say, redesigning the home with a smartphone, or have a shopper’s avatar try on clothes before purchase. In AR/VR headsets, depth information
acquired by ToF systems enables interactions via hand tracking, with virtual objects placed in the real world, more accurately merging the physical and digital worlds for an immersive experience. As innovations continue apace, we will
see even more life-changing functionalities and devices in the near future.
www.electronicsworld.co.uk February 2024 35
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