Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images.

Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. However, there are often some suspicious small cancer nests in DCIS, and it is difficult to diagnose the presence of intact myoepithelium by conventional hematoxylin and eosin (H&E) stained images. Although a variety of biomarkers are available for immunohistochemical (IHC) staining of myoepithelial cells, no single biomarker is consistently sensitive to all tumor lesions. Here, we introduced a new diagnostic method that provides rapid and accurate diagnosis of DCISM using multiphoton microscopy (MPM). Suspicious foci in H&E-stained images were labeled as regions of interest (ROIs), and the nuclei within these ROIs were segmented using a deep learning model. MPM was used to capture images of the ROIs in H&E-stained sections. The intensity of two-photon excitation fluorescence (TPEF) in the myoepithelium was significantly different from that in tumor parenchyma and tumor stroma. Through the use of MPM, the myoepithelium and basement membrane can be easily observed via TPEF and second-harmonic generation (SHG), respectively. By fusing the nuclei in H&E-stained images with MPM images, DCISM can be differentiated from suspicious small cancer clusters in DCIS. The proposed method demonstrated good consistency with the cytokeratin 5/6 (CK5/6) myoepithelial staining method (kappa coefficient = 0.818).

Real-time 3D-3D image fusion of CTA/CBCT roadmap fluoroscopy in the transcatheter mitral intervention.

Absence of periprocedural visualization of three-dimensional (3D) left heart anatomy and its surrounding structures in fluoroscopy may reduce the rate of successful transcatheter mitral valve repair. We proposed a multimodal imaging strategy based on 3D computed tomography (CT) angiography and 3D cone beam CT fusion images, which enabled real-time visual inspection of 3D cardiac structures on fluoroscopy, to optimize transcatheter mitral intervention. This new image fusion technology, together with standard transesophageal echocardiography guidance, improved the efficiency and safety of the procedure, and could be considered as a new workflow for transcatheter mitral valve intervention.

MR-CT image fusion method of intracranial tumors based on Res2Net.

BACKGROUND: Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. PURPOSE: The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. METHODS: This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. RESULTS: Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. CONCLUSION: The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer.

With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

Estimation of spectral similarities utilizing segmented regions' probability distribution in the block-optimized pan-sharpened image for material classification.

Pan-sharpening is an image fusion approach that combines the spectral information in multispectral (MS) images with the spatial properties of PAN (Panchromatic) images. This vital technique is used in categorization, detection, and other remote sensing applications. In the first step, the article focuses on increasing the finer spatial details in the MS image with PAN images using two levels of fusion without causing spectral deterioration. The suggested fusion method efficiently utilizes image transformation techniques and spatial domain image fusion methods. The luminance component of MS images typically contains spatial features that are not as detailed as the PAN images. A multiscale transform is applied to the intensity/luminance component and PAN image to introduce features into the intensity component. In the first level of processing, coefficients obtained from the non-subsampled contourlet transform are subjected to particle swarm optimization weighted block-based fusion. The second level of fusion is carried out using the concept of spatial frequency to reduce spectral distortion. Numerous reference and non-reference parameters are used to evaluate the sharpened image's quality. In the next step, the article focuses on designing an evaluation metric for analysing spectral distortion based on the Bhattacharyya coefficient and distance. The Bhattacharyya coefficient and distance are calculated for each segmented region to assess the sharpened images' quality. Spectral degradation analysis using proposed techniques can also be useful for analysing materials in the segmented regions. The research findings demonstrate that the spatial features of fused images obtained from the proposed technique increased with the least spectral degradation.

Image Fusion Method Based on Snake Visual Imaging Mechanism and PCNN.

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes.

Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study.

This study investigates the application of hyperspectral image space-spectral fusion technology in lithologic classification, using data from China's GF-5 and Europe's Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space-spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The "GSA+RF" method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.

Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A.

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3-0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios.

Integration of Sentinel-1A Radar and SMAP Radiometer for Soil Moisture Retrieval over Vegetated Areas.

NASA's Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA's Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields.

HyperFace: A Deep Fusion Model for Hyperspectral Face Recognition.

Face recognition has been well studied under visible light and infrared (IR) in both intra-spectral and cross-spectral cases. However, how to fuse different light bands for face recognition, i.e., hyperspectral face recognition, is still an open research problem, which has the advantages of richer information retention and all-weather functionality over single-band face recognition. Thus, in this research, we revisit the hyperspectral recognition problem and provide a deep learning-based approach. A new fusion model (named HyperFace) is proposed to address this problem. The proposed model features a pre-fusion scheme, a Siamese encoder with bi-scope residual dense learning, a feedback-style decoder, and a recognition-oriented composite loss function. Experiments demonstrate that our method yields a much higher recognition rate than face recognition using only visible light or IR data. Moreover, our fusion model is shown to be superior to other general-purpose image fusion methods that are either traditional or deep learning-based, including state-of-the-art methods, in terms of both image quality and recognition performance.

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion.

The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This paper proposes a fast and efficient recursive fusion neural network model to solve this complex problem that few people have touched. Specifically, we designed an attention module combining a traditional fusion knowledge prior with channel attention to extract modal-specific features efficiently. We used a shared attention layer to perform the early fusion of modal-shared features. Adopting parallel dilated convolution layers further reduces the network's parameter count. Our network is trained recursively, featuring minimal model parameters, and requires only a few training batches to achieve excellent fusion results. This significantly reduces the consumption of time, space, and computational resources during model training. We compared our method with nine SOTA methods on three public datasets, demonstrating our method's efficient training feature and good fusion results.

MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer.

The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis.

DCFNet: Infrared and Visible Image Fusion Network Based on Discrete Wavelet Transform and Convolutional Neural Network.

Aiming to address the issues of missing detailed information, the blurring of significant target information, and poor visual effects in current image fusion algorithms, this paper proposes an infrared and visible-light image fusion algorithm based on discrete wavelet transform and convolutional neural networks. Our backbone network is an autoencoder. A DWT layer is embedded in the encoder to optimize frequency-domain feature extraction and prevent information loss, and a bottleneck residual block and a coordinate attention mechanism are introduced to enhance the ability to capture and characterize the low- and high-frequency feature information; an IDWT layer is embedded in the decoder to achieve the feature reconstruction of the fused frequencies; the fusion strategy adopts the l1-norm fusion strategy to integrate the encoder's output frequency mapping features; a weighted loss containing pixel loss, gradient loss, and structural loss is constructed for optimizing network training. DWT decomposes the image into sub-bands at different scales, including low-frequency sub-bands and high-frequency sub-bands. The low-frequency sub-bands contain the structural information of the image, which corresponds to the important target information, while the high-frequency sub-bands contain the detail information, such as edge and texture information. Through IDWT, the low-frequency sub-bands that contain important target information are synthesized with the high-frequency sub-bands that enhance the details, ensuring that the important target information and texture details are clearly visible in the reconstructed image. The whole process is able to reconstruct the information of different frequency sub-bands back into the image non-destructively, so that the fused image appears natural and harmonious visually. Experimental results on public datasets show that the fusion algorithm performs well according to both subjective and objective evaluation criteria and that the fused image is clearer and contains more scene information, which verifies the effectiveness of the algorithm, and the results of the generalization experiments also show that our network has good generalization ability.

TGLFusion: A Temperature-Guided Lightweight Fusion Method for Infrared and Visible Images.

The fusion of infrared and visible images is a well-researched task in computer vision. These fusion methods create fused images replacing the manual observation of single sensor image, often deployed on edge devices for real-time processing. However, there is an issue of information imbalance between infrared and visible images. Existing methods often fail to emphasize temperature and edge texture information, potentially leading to misinterpretations. Moreover, these methods are computationally complex, and challenging for edge device adaptation. This paper proposes a method that calculates the distribution proportion of infrared pixel values, allocating fusion weights to adaptively highlight key information. It introduces a weight allocation mechanism and MobileBlock with a multispectral information complementary module, innovations which strengthened the model's fusion capabilities, made it more lightweight, and ensured information compensation. Training involves a temperature-color-perception loss function, enabling adaptive weight allocation based on image pair information. Experimental results show superiority over mainstream fusion methods, particularly in the electric power equipment scene and publicly available datasets.

Color Night Light Remote Sensing Images Generation Using Dual-Transformation.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and "NPP-VIIRS-like" night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

CIRF: Coupled Image Reconstruction and Fusion Strategy for Deep Learning Based Multi-Modal Image Fusion.

Multi-modal medical image fusion (MMIF) is crucial for disease diagnosis and treatment because the images reconstructed from signals collected by different sensors can provide complementary information. In recent years, deep learning (DL) based methods have been widely used in MMIF. However, these methods often adopt a serial fusion strategy without feature decomposition, causing error accumulation and confusion of characteristics across different scales. To address these issues, we have proposed the Coupled Image Reconstruction and Fusion (CIRF) strategy. Our method parallels the image fusion and reconstruction branches which are linked by a common encoder. Firstly, CIRF uses the lightweight encoder to extract base and detail features, respectively, through the Vision Transformer (ViT) and the Convolutional Neural Network (CNN) branches, where the two branches interact to supplement information. Then, two types of features are fused separately via different blocks and finally decoded into fusion results. In the loss function, both the supervised loss from the reconstruction branch and the unsupervised loss from the fusion branch are included. As a whole, CIRF increases its expressivity by adding multi-task learning and feature decomposition. Additionally, we have also explored the impact of image masking on the network's feature extraction ability and validated the generalization capability of the model. Through experiments on three datasets, it has been demonstrated both subjectively and objectively, that the images fused by CIRF exhibit appropriate brightness and smooth edge transition with more competitive evaluation metrics than those fused by several other traditional and DL-based methods.

Semantic-Aware Fusion Network Based on Super-Resolution.

The aim of infrared and visible image fusion is to generate a fused image that not only contains salient targets and rich texture details, but also facilitates high-level vision tasks. However, due to the hardware limitations of digital cameras and other devices, there are more low-resolution images in the existing datasets, and low-resolution images are often accompanied by the problem of losing details and structural information. At the same time, existing fusion algorithms focus too much on the visual quality of the fused images, while ignoring the requirements of high-level vision tasks. To address the above challenges, in this paper, we skillfully unite the super-resolution network, fusion network and segmentation network, and propose a super-resolution-based semantic-aware fusion network. First, we design a super-resolution network based on a multi-branch hybrid attention module (MHAM), which aims to enhance the quality and details of the source image, enabling the fusion network to integrate the features of the source image more accurately. Then, a comprehensive information extraction module (STDC) is designed in the fusion network to enhance the network's ability to extract finer-grained complementary information from the source image. Finally, the fusion network and segmentation network are jointly trained to utilize semantic loss to guide the semantic information back to the fusion network, which effectively improves the performance of the fused images on high-level vision tasks. Extensive experiments show that our method is more effective than other state-of-the-art image fusion methods. In particular, our fused images not only have excellent visual perception effects, but also help to improve the performance of high-level vision tasks.

Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images.

Latent Low-Rank Representation (LatLRR) has emerged as a prominent approach for fusing visible and infrared images. In this approach, images are decomposed into three fundamental components: the base part, salient part, and sparse part. The aim is to blend the base and salient features to reconstruct images accurately. However, existing methods often focus more on combining the base and salient parts, neglecting the importance of the sparse component, whereas we advocate for the comprehensive inclusion of all three parts generated from LatLRR image decomposition into the image fusion process, a novel proposition introduced in this study. Moreover, the effective integration of Convolutional Neural Network (CNN) technology with LatLRR remains challenging, particularly after the inclusion of sparse parts. This study utilizes fusion strategies involving weighted average, summation, VGG19, and ResNet50 in various combinations to analyze the fusion performance following the introduction of sparse parts. The research findings show a significant enhancement in fusion performance achieved through the inclusion of sparse parts in the fusion process. The suggested fusion strategy involves employing deep learning techniques for fusing both base parts and sparse parts while utilizing a summation strategy for the fusion of salient parts. The findings improve the performance of LatLRR-based methods and offer valuable insights for enhancement, leading to advancements in the field of image fusion.

Infrared and Visual Image Fusion Based on a Local-Extrema-Driven Image Filter.

The objective of infrared and visual image fusion is to amalgamate the salient and complementary features of the infrared and visual images into a singular informative image. To accomplish this, we introduce a novel local-extrema-driven image filter designed to effectively smooth images by reconstructing pixel intensities based on their local extrema. This filter is iteratively applied to the input infrared and visual images, extracting multiple scales of bright and dark feature maps from the differences between continuously filtered images. Subsequently, the bright and dark feature maps of the infrared and visual images at each scale are fused using elementwise-maximum and elementwise-minimum strategies, respectively. The two base images, representing the final-scale smoothed images of the infrared and visual images, are fused using a novel structural similarity- and intensity-based strategy. Finally, our fusion image can be straightforwardly produced by combining the fused bright feature map, dark feature map, and base image together. Rigorous experimentation conducted on the widely used TNO dataset underscores the superiority of our method in fusing infrared and visual images. Our approach consistently performs on par or surpasses eleven state-of-the-art image-fusion methods, showcasing compelling results in both qualitative and quantitative assessments.

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry.

Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or an individual's health conditions. Combining multimodal camera data with shape data from 3D sensors is a challenging issue. Multimodal cameras, e.g., hyperspectral cameras, or cameras outside the visible light spectrum, e.g., thermal cameras, lack strongly in terms of resolution and image quality compared with state-of-the-art photo cameras. In this article, a new method is demonstrated to superimpose multimodal image data onto a 3D model created by multi-view photogrammetry. While a high-resolution photo camera captures a set of images from varying view angles to reconstruct a detailed 3D model of the scene, low-resolution multimodal camera(s) simultaneously record the scene. All cameras are pre-calibrated and rigidly mounted on a rig, i.e., their imaging properties and relative positions are known. The method was realized in a laboratory setup consisting of a professional photo camera, a thermal camera, and a 12-channel multispectral camera. In our experiments, an accuracy better than one pixel was achieved for the data fusion using multimodal superimposition. Finally, application examples of multimodal 3D digitization are demonstrated, and further steps to system realization are discussed.