Adaptive Interaction-Based Multi-view 3D Object Reconstruction

This paper introduces an end-to-end deep learning framework for multi-view 3D object reconstruction. The algorithm constructs an adaptive multiview combination module in the 2D encoding through calculating the feature correlation of pixel points of each view, allowing each view contains the feature information of other views. It addresses the issue of inconsistent object reconstruction resulting from input images being presented in different orders. Additionally, a voxel refinement loss is employed to produce a comprehensive 3D voxel and establishes an adaptive voxel discrimination module for 3D voxel calibration. This reduces the production of superfluous voxels in the 3D voxel and enhances the completeness of the reconstruction. Extensive validation using the ShapeNet synthetic dataset and the Pix3D real-world dataset demonstrates that the proposed algorithm outperforms existing methods.