Linear Model Hashing and Batch RANSAC for Rapid and Accurate Object Recognition

This paper proposes a joint feature-based model indexing and geometric constraint based alignment pipeline for efficient and accurate recognition of 3D objects from a large model database. Traditional approaches either first prune the model database using indexing without geometric alignment or directly perform recognition based alignment. The indexing based pruning methods without geometric constraints can miss the correct models under imperfections such as noise, clutter and obscurations. Alignment based verification methods have to linearly verify each model in the database and hence do not scale up. The proposed techniques use spin images as semi-local shape descriptors and Locality-Sensitive Hashing (LSH) to index into a joint spin image database for all the models. The indexed models represented in the pruned set are further pruned using progressively complex geometric constraints. A simple geometric configuration of multiple spin images, for instance a doublet, is first used to...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Ying Shan, Bogdan Matei, Harpreet S. Sawhney, Rakesh Kumar, Daniel F. Huber, Martial Hebert
• Keywords: Complex Geometric Constraints, Computer Vision, CVPR 2004, Euclidean Geometric Constraints, Geometric Alignment, Geometric Indexing Techniques, Simple Geometric Configuration