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WiMi Announced Multi-View 3D Reconstruction Algorithm Based on Semantic Segmentation

WiMi Announced Multi-View 3D Reconstruction Algorithm Based on Semantic Segmentation

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that its multi-view 3D reconstruction algorithm based on semantic segmentation combine semantic segmentation and 3D reconstruction is developed, aiming to achieve more accurate 3D reconstruction results. In traditional multi-view 3D reconstruction algorithm, only the geometric information of the image is usually considered to reconstruct the 3D scene by extracting feature points or matching features from multiple views, while the utilization of semantic information is neglected, resulting in reconstruction results that lack the understanding and interpretation of the scene semantics. With the rapid development of deep learning, semantic segmentation technology has gradually become a popular research direction in the field of computer vision. Semantic segmentation technology can assign each pixel in an image to a different semantic category, to realize accurate segmentation and semantic understanding of objects in an image. The multi-view 3D reconstruction algorithm based on semantic segmentation researched of WiMi combines semantic segmentation technology with the 3D reconstruction method to realize accurate reconstruction and semantic understanding of 3D scenes.

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Semantic information can provide more contextual and semantic constraints, and by applying semantic segmentation technology to multi-view 3D reconstruction, more accurate semantic information can be obtained during the reconstruction process, thus improving the accuracy and comprehensibility of the reconstruction results. In practical applications, the multi-view 3D reconstruction based on semantic segmentation can be applied to 3D scene reconstruction to provide users with a more realistic experience. For example, when reconstructing a building, semantic segmentation can assign different areas to different categories, such as walls, windows, doors, etc., so that the reconstruction results more accurately reflect the structure and composition of the building. In addition, the multi-view 3D reconstruction algorithm based on semantic segmentation can be applied to other fields, such as automatic driving, virtual reality, augmented reality, etc., in order to achieve a more accurate understanding and simulation of the scene. The development of multi-view 3D reconstruction algorithms based on semantic segmentation has important research and application value.

In the process of applying WiMi’s multi-view 3D reconstruction algorithm based on semantic segmentation, the input multi-view image needs to be pre-processed and feature extraction first, and the pre-processing mainly includes operations such as image denoising and image enhancement, and then the feature points are extracted for each image to get the feature map. Then the feature map is semantically segmented using a semantic segmentation network to get the semantic label of each pixel. Then, according to the semantic label, the matching pixels are found in different views and the correspondence between the feature points is established. Based on the results of pixel matching, the 3D point cloud is reconstructed using a triangulation algorithm. Finally, the reconstructed 3D point cloud is optimized, including operations such as removing outlier points and filling in missing regions, and finally, the 3D reconstruction results are obtained. The algorithm can realize accurate 3D reconstruction in multi-view scenes, and it can provide richer scene information through semantic segmentation to improve the accuracy of reconstruction.

Compared to traditional 3D reconstruction algorithms, the multi-view 3D reconstruction algorithm based on semantic segmentation can perform image processing and computation more efficiently. By utilizing semantic information, the algorithm can reduce unnecessary computation and processing, thus increasing the running speed of the algorithm. In addition, semantic segmentation can help the algorithm to better utilize the ability of parallel computing, which further improves the efficiency of the algorithm.

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By using semantic segmentation technique, the algorithm can also better understand the object boundaries and structural information in the image, thus improving the accuracy of 3D reconstruction. By assigning each pixel to its corresponding semantic category, the algorithm can better distinguish the boundaries between different objects and can better restore the details of the objects. In addition, the semantic segmentation technique can help the algorithm to better deal with noise and occlusion in the image. By segmenting the image into semantic regions, occluded objects can be better recognized and handled, thus improving the robustness of the reconstruction. In addition, semantic segmentation can help the algorithms to better deal with lighting variations and poor image quality.

WIiMi’s multi-view 3D reconstruction algorithm based on semantic segmentation has obvious advantages in terms of accuracy, and efficiency. These advantages enable the algorithm to better restore real-world 3D scenes in practical applications and to better cope with various complex situations in the real world.

Currently, deep learning has achieved remarkable results in the field of semantic segmentation and 3D reconstruction. In the future, WiMi will explore how to combine deep learning methods with traditional geometric computation methods, fully utilize the advantages of both, and improve the performance and effect of multi-view 3D reconstruction algorithms based on semantic segmentation.

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