WeedGan: a novel generative adversarial network for cotton weed identification

Sharma, Vivek; Tripathi, Ashish; Mittal, Himanshu; Parmar, Abhishek D.; Soni, Ashutosh; Amarwal, Rahul

doi:10.1007/s00371-022-02742-5

Cited by 11 publications

(1 citation statement)

References 72 publications

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“…Boolean operations on geometric models are a core element in the field of computeraided design and graphics, and are the basic algorithms for constructing solid geometric models. They are used in the construction industry [5], manufacturing industry [6,7], computer vision [8][9][10], graphics [11,12], and other scientific research fields [13,14]. Boolean operations are needed to combine and truncate geometric models to generate complex 3D models; therefore, Boolean operations have important research significance.…”

Section: Introductionmentioning

confidence: 99%

Simple and Robust Boolean Operations for Triangulated Surfaces

et al. 2023

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Boolean operations on geometric models are important in numerical simulation and serve as essential tools in the fields of computer-aided design and computer graphics. The accuracy of these operations is heavily influenced by finite precision arithmetic, a commonly employed technique in geometric calculations, which introduces numerical approximations. To ensure robustness in Boolean operations, numerical methods relying on rational numbers or geometric predicates have been developed. These methods circumvent the accumulation of rounding errors during computation, thus preserving accuracy. Nonetheless, it is worth noting that these approaches often entail more intricate operation rules and data structures, consequently leading to longer computation times. In this paper, we present a straightforward and robust method for performing Boolean operations on both closed and open triangulated surfaces. Our approach aims to eliminate errors caused by floating-point operations by relying solely on entity indexing operations, without the need for coordinate computation. By doing so, we ensure the robustness required for Boolean operations. Our method consists of two main stages: (1) Firstly, candidate triangle intersection pairs are identified using an octree data structure, and then parallel algorithms are employed to compute the intersection lines for all pairs of triangles. (2) Secondly, closed or open intersection rings, sub-surfaces, and sub-blocks are formed, which is achieved entirely by cleaning and updating the mesh topology without geometric solid coordinate computation. Furthermore, we propose a novel method based on entity indexing to differentiate between the union, subtraction, and intersection of Boolean operation results, rather than relying on inner and outer classification. We validate the effectiveness of our method through various types of Boolean operations on triangulated surfaces.

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