Linear model hashing and batch RANSAC for rapid and accurate object recognition

• Published in: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004 Vol. 2; p. II
• Main Authors: Shan, Y., Matei, B., Sawhney, H.S., Kumar, R., Huber, D., Hebert, M.
• Format: Conference Proceeding
• Language: English
• Published: Los Alamitos, California IEEE 2004; IEEE Computer Society
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Image databases; Indexes; Indexing; Information systems. Data bases; Memory organisation. Data processing; Noise shaping; Object recognition; Pattern recognition. Digital image processing. Computational geometry; Pipelines; Polynomials; Shape; Software; Solid modeling; Spatial databases; Pipeline processor; Image processing; Very large databases; Clutter; Modeling; Geometrical model; Linear model; Multiple image; Alignment; Constructive geometry; Model matching; Database; Batch production; Defect; Computer vision; Locality; Image retrieval; Euclidean theory; Image databank; Pattern recognition; Distributed system; Object recognition; Program verification; Batch process; Hashing; Content-based retrieval; Indexing
• ISBN: 9780769521589; 0769521584
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2004.1315153

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 check for geometric consistency. Subsequently, full Euclidean geometric constraints are applied using RANSAC-based techniques on the pruned spin images and the models to verify specific object identity. As a result, the combined indexing and geometric alignment based pipeline is able to focus on matching the most promising models, and generate far less pose hypotheses while maintaining the same level of performance as the sequential alignment based recognition. Furthermore, compared to geometric indexing techniques like geometric hashing, the construction time and storage complexity for the proposed technique remains linear in the number of features rather than higher order polynomial. Experiments on a 56 3D model database show promising results.