Vicente, S; Carreira, J; Agapito, L; Batista, J; (2014) Reconstructing PASCAL VOC. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. (pp. pp. 41-48). IEEE: Columbus, OH, USA.

Preview

Text Vicente_Reconstructing_PASCAL_VOC_2014_CVPR_paper.pdf - Accepted Version Download (7MB) | Preview

Abstract

We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations. Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion, then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions. The visual hull sampling process attempts to intersect an object's projection cone with the cones of minimal subsets of other similar objects among those pictured from certain vantage points. We show that our method is able to produce convincing per-object 3D reconstructions on one of the most challenging existing object-category detection datasets, PASCAL VOC. Our results may re-stimulate once popular geometry-oriented model-based recognition approaches.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as CSVXMLJSON

Downloads by country - last 12 months

Export as CSVJSONXML

Archive Staff Only

View Item