Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images

Tian, Jiawei; Ma, Botao; Lu, Siyu; Yang, Bo; Liu, Shan; Yin, Zhengtong

doi:10.3390/electronics12183799

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Frame differencing is simple to implement, has low computational requirements, and exhibits strong adaptability and robustness in dynamic environments. However, in the presence of large areas of similar grayscale values on the surface of the moving object, frame differencing may result in holes in the image [31,32]. In recent years, deep learning technology has shown its remarkable feature extraction capabilities.…”

Section: Motion Object Extractionmentioning

confidence: 99%

Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows

Wang,

Chen,

2023

Sensors

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Various statistical data indicate that mobile source pollutants have become a significant contributor to atmospheric environmental pollution, with vehicle tailpipe emissions being the primary contributor to these mobile source pollutants. The motion shadow generated by motor vehicles bears a visual resemblance to emitted black smoke, making this study primarily focused on the interference of motion shadows in the detection of black smoke vehicles. Initially, the YOLOv5s model is used to locate moving objects, including motor vehicles, motion shadows, and black smoke emissions. The extracted images of these moving objects are then processed using simple linear iterative clustering to obtain superpixel images of the three categories for model training. Finally, these superpixel images are fed into a lightweight MobileNetv3 network to build a black smoke vehicle detection model for recognition and classification. This study breaks away from the traditional approach of “detection first, then removal” to overcome shadow interference and instead employs a “segmentation-classification” approach, ingeniously addressing the coexistence of motion shadows and black smoke emissions. Experimental results show that the Y-MobileNetv3 model, which takes motion shadows into account, achieves an accuracy rate of 95.17%, a 4.73% improvement compared with the N-MobileNetv3 model (which does not consider motion shadows). Moreover, the average single-image inference time is only 7.3 ms. The superpixel segmentation algorithm effectively clusters similar pixels, facilitating the detection of trace amounts of black smoke emissions from motor vehicles. The Y-MobileNetv3 model not only improves the accuracy of black smoke vehicle recognition but also meets the real-time detection requirements.

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Section: Motion Object Extractionmentioning

confidence: 99%

Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows

Wang,

Chen,

2023

Sensors

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Visualization Challenges of Virtual Reality 3D Images in New Media Environments

Sun,

Zhang,

Yan

2024

Applied Mathematics and Nonlinear Sciences

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This paper proposes a three-dimensional image visualization process to face-drawing three-dimensional image reconstruction algorithm to obtain the data field with three-dimensional space, using color adjustment based on global color correction and local Poisson fusion to optimize the splicing seams between the texture color blocks and updating the visualization technology of three-dimensional images. Divide the digital display design and create a virtual reality visualization display using 3D modeling in combination with the new media environment. Propose design steps to visualize virtual reality three-dimensional images in the new media environment by combining the key algorithms of three-dimensional image visualization from the previous section. Combined with the application of new media displaying 3D images, the concept of artifact shape in reconstructed images is proposed to analyze the quality of 3D image reconstruction by taking the Herman model and Sheep-Logan model as the research object. Test experiments are conducted to examine the visual impact of texture mapping algorithms, and different sampling intervals are set to measure the drawing time of 3D reconstruction. For the data size and number of pictures of other organizations, the processing time of the 3D image reconstruction algorithm based on surface drawing is no more than 2s. The denser the sampling points are, the higher the degree of fitting, the more complete the preservation of isosurface information is, the finer the effect of 3D reconstruction, and the higher the quality of the image.

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A Scaled Monocular 3D Reconstruction Based on Structure from Motion and Multi-View Stereo

Zhan,

Yang,

Jiang

et al. 2024

Electronics

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Three-dimensional digital modeling at actual scales is essential for digitally preserving cultural relics. While 3D reconstruction using a monocular camera offers a cost-effective solution, the lack of scale information in the resulting models limits their suitability for geometric measurements. Objects with monotonous textures, such as batteries, pose additional challenges due to insufficient feature points, increasing positional uncertainty. This article proposes a method incorporating point and line features to address the scale ambiguity in multi-view 3D reconstruction using monocular cameras. By pre-measuring the lengths of multiple sets of real line segments, building a lookup table, and associating the line features in different images, the table was input into the improved reconstruction algorithm to further optimize the scale information. Experimental results on real datasets showed that the proposed method outperformed the COLMAP method by 70.82% in reconstruction accuracy, with a scale recovery reaching millimeter-level accuracy. This method is highly generalizable, cost-effective, and supports lightweight computation, making it suitable for real-time operation on a CPU.

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Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images

Cited by 3 publications

References 35 publications

Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows

Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows

Visualization Challenges of Virtual Reality 3D Images in New Media Environments

A Scaled Monocular 3D Reconstruction Based on Structure from Motion and Multi-View Stereo

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