3D sensor Market Analysis Estimated New Growth Story and Forecast to 2028

Posted by kartik joshi on October 10th, 2022

3D sensor Market along with the estimates and forecasts of the revenue and market share analysis and also spots the significant 3D sensor players in the market and their key developments.

The global 3D sensor market is accounted to US$ 4,805.7 Mn in 2018 and is expected to grow at a CAGR of 32.5% during the forecast period 2019 – 2028, to account to US$ 71,914.2 Mn by 2028. The 3D Time-of-Flight (ToF) sensor have materialized as a promising three-dimensional (3D) sensing technology that can be manufactured economically in a compact size. In ToF, an infrared strobe emits a bright, short pulse, and a custom detector with a very fast shutter speed measures the time that the light travels before hitting an object. However, current state-of-the-art ToF sensors suffer from low spatial resolution due to physical limitations in the fabrication process.

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3D sensors use active-range sensors that provide depth images at high frame rates. These cameras are equipped with an IR light source that illuminates the field, and a CMOS/CCD sensor that captures the reflected IR light. The depth measurement is based on the ToF principle; thus, the depth is proportional to the time spent by the IR signal to reach the object or filed of observation and come back. The depth measurements obtained for each pixel of the sensor together produce a depth image. The fast acquisition of depth images is of great use in a wide range of end-user verticals, such as robotics, human–machine interaction, and scene modeling. Unfortunately, many of the currently available ToF cameras have low resolutions and are affected by different measuring errors, including noise caused by sensors, systematic wiggling error due to the difficulty of generating sinusoidal signals, a non-linear depth offsets dependent on reflectivity and integration-time, and the flying pixels generated by the superposition of signals at depth inhomogeneities (edges). As a result, ToF sensors provide accurate and precise depth measurements. The currently available multi-ToF sensor systems focus on combining depth images to build 3D reconstructions by relying on occupancy probability grids or registering the point clouds generated from different views, etc.

The ToF techniques have been in use for more than a decade for ranging purposes. SONAR and RADAR are the two techniques that exploit sound and radio signals of ToF principles, particularly in aerospace and aeronautic end-user verticals. More recently, with the improvement and the maturity of electronic devices, it has been possible to employ light signals for ToF systems. End-user verticals using such a system are numerous, especially in industrial and consumer fields. In general, there are two techniques for measuring the distance with modern ToF sensors: pulsed-modulation or continuous-wave (CW) modulation. Advanced ToF systems deploy multi-frequency technologies, combining more modulation frequencies.

AI is boosting innovation in every industry, providing opportunities for new ideas that are accelerating digital transformation. With some of the biggest tech players from the US and China, such as Google, Amazon, Apple, Facebook, Microsoft, IBM, Baidu, Alibaba, and Tencent, at the forefront of the AI-related R&D, this technology is expected to change the face of internet end-user verticals in the coming future. AI will be the key factor in transforming the present businesses and digital end-user verticals by improving customer experiences based on their behavior. The various depth-sensing technologies are already being implemented in a slew of end-user verticals, such as industrial robotics and autonomous vehicles, for the last few years. The need for accurate mapping and plotting is high for achieving highly accurate autonomous operations. The depth-sensing technologies, when coupled with the edge AI, can be used for harnessing large volumes of data that can be processed and modeled into extremely useful actionable insights.

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