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Objective As a typical numerical representation of geometric models, the triangular mesh is widely used in additive manufacturing, inverse design, and finite element analysis. The triangular mesh model is directly reconstructed based on industrial CT images, which allows for the reconstruction of 3D representations of parts with complicated internal cavity structures. However, current algorithms for reconstructing triangular mesh models based on industrial CT images, for example, marching cube (MC) algorithm, have problems such as loss of sharp features, many longnarrow triangles, and a large number of triangular surfaces. In this paper, we propose an adaptive 3D mesh model reconstruction method to simultaneously address these issues while improving the quality of the reconstructed triangular mesh model from industrial CT images.Methods First, a bilateral filter and an OTSU algorithm are utilized to preprocess industrial CT images, so as to denoise and determine the value of the isosurface. Second, an octree structure is used to confirm the voxels; the octree is created topdown recursively, while nonboundary voxels are deleted to save storage space. The quadratic error function (QEF) is then applied to each boundary voxel of the octree to produce a feature point, and the octree is simplified by merging the feature points from the bottom up. Third, a quadrilateral formed by four adjacent feature points is divided into two triangular meshes. In order to validate the performance of the proposed algorithm, experiments are performed using a cubic dataset and two groups of real industrial CT images. Results and DiscussionsTo begin with, a cubic dataset with no noise is utilized and reconstructed using the MC algorithm and the approach proposed in this study, as shown in Fig. 7. Sharp features including angles are lost by the MC algorithm [Fig. 7(a)]. The approach in this article not only generates the cube's edges but also a smaller triangular mesh to

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