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ECCV
2010
Springer

Semantic Segmentation of Urban Scenes Using Dense Depth Maps

11 years 10 months ago
Semantic Segmentation of Urban Scenes Using Dense Depth Maps
In this paper we present a framework for semantic scene parsing and object recognition based on dense depth maps. Five viewindependent 3D features that vary with object class are extracted from dense depth maps at a superpixel level for training a classifier using randomized decision forest technique. Our formulation integrates multiple features in a Markov Random Field (MRF) framework to segment and recognize different object classes in query street scene images. We evaluate our method both quantitatively and qualitatively on the challenging Cambridge-driving Labeled Video Database (CamVid). The result shows that only using dense depth information, we can achieve overall better accurate segmentation and recognition than that from sparse 3D features or appearance, or even the combination of sparse 3D features and appearance, advancing state-of-the-art performance. Furthermore, by aligning 3D dense depth based features into a unified coordinate frame, our algorithm can handle the specia...
Chenxi Zhang, Liang Wang, Ruigang Yang
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ECCV
Authors Chenxi Zhang, Liang Wang, Ruigang Yang
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