BEIJING, Jan. 20, 2023 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO) ("HOLO" or the "Company"), a Hologram Digital Twins Technology provider, today announced that it developed a point cloud denoising algorithm for the real-time 3D holographic reconstruction of single-photon LiDAR data. The algorithm is the result of the Company's independent research and development, which is conducive to further improving the Company's intellectual property protection system, maintaining its technological leadership, and enhancing its core competitiveness.
Although 3D holographic LiDAR point cloud imaging continues to evolve rapidly, currently available computational imaging algorithms are often too slow, insufficiently detailed, or require extremely high arithmetic power, and even CNN-based (convolutional neural network) algorithms for estimating scene depth struggle to meet real-time requirements after training. HOLO proposes a new algorithm structure that meets the requirements of speed, robustness, and scalability. The algorithm applies a point cloud denoising tool for computer graphics and can efficiently model the target surface as a 2D manifold embedded in 3D space. This algorithm can merge information about the observed model, such as Poisson noise, the presence of bad pixels, compressed sensing, etc. This algorithm also uses stream modeling tools for computer graphics and can process tens of frames per second by selecting massively parallel noise reducers. HOLO's algorithm consists of three main steps: depth update, intensity update, and background update.
Depth update: Gradient steps are taken for depth variables with point clouds denoised using the point set surface algorithm. The update is operated in a coordinate system in 3D holographic space. Adaptation is performed on smooth continuous surfaces under the control of the kernel. In contrast to conventional depth image denoising, HOLO's point cloud denoising can handle an arbitrary number of surfaces per pixel, regardless of the format. In addition, all 3D points are processed in parallel, resulting in short computing times. In addition, all 3D points are processed in parallel, significantly reducing computing time.
Intensity update: Gradient steps are taken by targeting the coordinates of individual pixels in 3D holographic space to reduce noise. In this way, only the correlation between points within the same surface needs to be considered. The nearest low-pass filter is used for each point. This step considers only local correlations and processes all points in parallel. After the denoising step, points below a given intensity threshold, i.e., the minimum permissible reflectance, are removed.
Background update: This depends on the characteristics of the LiDAR system and is similar to intensity and depth updates. In a double-Bragg-grating scanning system, the laser source and the single photon detector are not coaxial, and the background counts are not necessarily spatially correlated. Therefore, no spatial regularization is applied to the background, in which case the noise reduction operation is simplified to a constant equation. Background detection is similar to passive images in single-Bragg-grating scanning systems and LiDAR arrays. In this case, spatial regularization helps to improve the estimated value. Therefore, an off-the-shelf image-denoising algorithm with low computational complexity can be used.
HOLO's real-time 3D holographic reconstruction based on single photon data adopts a new computational framework. This framework allows the 3D reconstruction of complex outdoor scenes with a processing time of about 10-20 milliseconds by combining statistical models with highly expandable computational tools of computer imaging technology. The algorithm developed by HOLO can handle every pixel surface, allowing target detection and imaging in complex scenes. It also enables stable real-time target reconstruction of complex moving scenes, paving the way for implementing video-rate single photon LiDAR technology for 3D holographic imaging applications.
3D holographic scene reconstruction has many applications, such as autonomous navigation and environmental detection. It has several subdivision fields, such as RGB-D sensors for emissivity imaging, stereo imaging, or full waveform LiDAR 3D holographic imaging. HOLO's single photon LiDAR technology solution has several outstanding advantages over traditional solutions. It is a safe laser light source with low power consumption and high sensitivity and enables the reconstruction of high fractional 3D holographic images in high scattering underwater environments or extremely foggy environments.
HOLO has been an industry leader in point cloud algorithms for holographic LiDAR and a service provider for many well-known new energy vehicle companies, as well as ADAS vehicle control system integrators and device developers. HOLO cooperates with automotive manufacturers in exploring advanced technologies and is seeking comprehensive digital sensing solutions to accelerate and scale the adoption of its autonomous driving technologies. HOLO's holographic LiDAR leverages this market trend to provide an excellent solution for large-scale autonomous driving mass production.
About MicroCloud Hologram Inc.
MicroCloud Hologram Inc. (NASDAQ:HOLO) engages in the research and development, and application of holographic technology. MicroCloud Hologram technology services include holographic light detection and ranging (LiDAR) solutions based on holographic technology, holographic LiDAR point cloud algorithms architecture design, technical holographic imaging solutions, holographic LiDAR sensor chip design, and holographic vehicle intelligent vision technology to service customers that provide holographic advanced driver assistance systems (ADAS). MicroCloud Hologram provides its holographic technology services to its customers worldwide. MicroCloud Hologram also provides holographic digital twin technology services and has a proprietary holographic digital twin technology resource library. MicroCloud holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology.
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