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The 3D reconstruction process is very important in a variety of computer vision applications. Bundle adjustment has a significant impact on 3D reconstruction processes, namely in Simultaneously Localization and Mapping (SLAM) and Structure from Motion (SfM). Bundle adjustment, which optimizes camera parameters and 3D points as a very important final stage, suffers from memory and efficiency requirements in very large-scale reconstruction. Multi-objective optimization (MOO) is used in solving a variety of realistic engineering problems. Multi-Objective Particle Swarm Optimization (MOPSO) is regarded as one of the state of the art for meta-heuristic MOO. MOPSO has utilized the concept of crowding distance as a measure to differentiate between solutions in the search space and provide a high level of exploration. However, this method ignores the direction of the exploration which is not sufficient to effectively explore the search space. In addition, MOPSO starts the search from a fully randomly initialized swarm without taking any prior knowledge about the initial guess into account, which is considered impractical in applications where we can estimate initial values for solutions like bundle adjustment. In this paper, we introduced a novel hybrid MOPSO-based bundle adjustment algorithm that takes advantage of initial guess, angle quantization technique, and traditional optimization algorithms like RADAM to improve the mobility of MOPSO solutions; the results showed that our algorithm can help improve the accuracy and efficiency of bundle adjustment (BA).

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A Novel Bundle Adjustment Approach Based on Guess-Aided and Angle Quantization Multiobjective Particle Swarm Optimization (GAMOPSO) for 3D Reconstruction Applications