Probabilistic 3D object recognition with both positive and negative evidences

Lee, Sukhan; Lu, Zhaojin; Kim, H

doi:10.1109/iccv.2011.6126518

Cited by 4 publications

(1 citation statement)

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“…The shape descriptors have been recently evaluated in [4]. One exception is Lee et al [29] who utilise lines, but that is particularly suitable for their objects of interest (boxes). Hybrids of 3D shape and 2D texture descriptors were proposed by Hu and Zhu [30] and Kang et al [31].…”

Section: Related Workmentioning

confidence: 99%

Indoor Objects and Outdoor Urban Scenes Recognition by 3D Visual Primitives

Kämäräinen

Buch

et al. 2015

Computer Vision - ACCV 2014 Workshops

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Object detection, recognition and pose estimation in 3D images have gained momentum due to availability of 3D sensors (RGB-D) and increase of large scale 3D data, such as city maps. The most popular approach is to extract and match 3D shape descriptors that encode local scene structure, but omits visual appearance. Visual appearance can be problematic due to imaging distortions, but the assumption that local shape structures are sufficient to recognise objects and scenes is largely invalid in practise since objects may have similar shape, but different texture (e.g., grocery packages). In this work, we propose an alternative appearance-driven approach which first extracts 2D primitives justified by Marr's primal sketch, which are "accumulated" over multiple views and the most stable ones are "promoted" to 3D visual primitives. The 3D promoted primitives represent both structure and appearance. For recognition, we propose a fast and effective correspondence matching using random sampling. For quantitative evaluation we construct a semi-synthetic benchmark dataset using a public 3D model dataset of 119 kitchen objects and another benchmark of challenging street-view images from 4 different cities. In the experiments, our method utilises only a stereo view for training. As the result, with the kitchen objects dataset our method achieved almost perfect recognition rate for ±10 • camera view point change and nearly 90% for ±20 • , and for the streetview benchmarks it achieved 75% accuracy for 160 street-view images pairs, 80% for 96 street-view images pairs, and 92% for 48 street-view image pairs.

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Section: Related Workmentioning

confidence: 99%