Image-based object reconstruction using run-length representation

This paper presents an image-based object reconstruction with a low memory footprint using run-length representation. While conventional volume-based approaches, which utilize voxels as primitives, are intuitive and easy to manipulate 3D data, they require a large amount of memory and computation during the reconstruction process. To overcome these burdens, this paper uses 3D runs to represent a 3D object and reconstructs each 3D run from multi-view silhouettes with a small amount of memory. The proposed geometry reconstruction is also computationally inexpensive, as it processes multiple voxels simultaneously. And for the compatibility with the conventional data formats, generation of polygonal 3D meshes from the reconstructed 3D runs is proposed as well. Lastly, texture mapping is proposed to additionally reduce the amount of memory for object reconstruction. The proposed reconstruction scheme has been simulated using various types of multi-view datasets. The results show that the proposed method performs object reconstruction with a smaller amount of memory and computation than voxel-based approaches.