Dense Points Aided Performance Evaluation Criterion of Human Obsevation for Image-based 3D Reconstruction

3D reconstruction is widely used in various scenarios such as virtual simulation and unmanned driving. Correspondingly, the requirements for its quality have been greatly improved. This paper presents a series of performance evaluation criteria based on the internal distribution of dense point cloud. In the definition of the assessment, the amount of point cloud information, uniform distribution, and the level of structural recurrence are considered. Using the criterion proposed in this paper, the pros and cons of the 3D reconstruction result can be comprehensively evaluated in comparison experiments of improving certain steps or using different methods. The design can further guide directions for optimization research and subsequent application. The experimental results show that adding the proposed standards based on general assessments can effectively reflect the quality of the reconstruction model. The performance evaluation criteria are scientific and practical.