JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge in fragment reassembly is to reliably compute and identify correct pairwise matching, for which most existing algorithms use handcrafted features, and hence, cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, we modify the commonly adopted greedy edge selection strategies to two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially those challenging ones with many fragment pieces

Sparse3D: A new global model for matching sparse RGB-D dataset with small inter-frame overlap

We present a novel 3D global matching algorithm, Sparse3D, to handle the challenging reconstruction of RGB-D datasets whose inter-frame overlap is small due to insufficient temporal sampling or fast camera movement. To support a more reliable reconstruction, two major technical components are proposed: (1) pairwise alignment using a set of complementary features, and (2) a novel global model for alignment pruning and pose optimization. We examine the effectiveness of our algorithm on multiple benchmark datasets under various inter-frame overlap, and demonstrate it better reliability over existing RGB-D reconstruction algorithms