Journal ArticleDOI
Extracting 3D Layout From a Single Image Using Global Image Structures
TLDR
This paper proposes an approach that first predicts the global image structure, and then uses the global structure for fine-grained pixel-level 3D layout extraction, and shows that employing the 3D structure prior information yields accurate 3D scene layout segmentation.Abstract:
Extracting the pixel-level 3D layout from a single image is important for different applications, such as object localization, image, and video categorization. Traditionally, the 3D layout is derived by solving a pixel-level classification problem. However, the image-level 3D structure can be very beneficial for extracting pixel-level 3D layout since it implies the way how pixels in the image are organized. In this paper, we propose an approach that first predicts the global image structure, and then we use the global structure for fine-grained pixel-level 3D layout extraction. In particular, image features are extracted based on multiple layout templates. We then learn a discriminative model for classifying the global layout at the image-level. Using latent variables, we implicitly model the sublevel semantics of the image, which enrich the expressiveness of our model. After the image-level structure is obtained, it is used as the prior knowledge to infer pixel-wise 3D layout. Experiments show that the results of our model outperform the state-of-the-art methods by 11.7% for 3D structure classification. Moreover, we show that employing the 3D structure prior information yields accurate 3D scene layout segmentation.read more
Citations
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Journal ArticleDOI
Image scene geometry recognition using low-level features fusion at multi-layer deep CNN
TL;DR: A novel model of image scene geometry recognition in which the low-level handcrafted features are integrated with deep CNN multi-stage features (HF-MSF) by using the feature-fusion and score-level fusion strategies is proposed.
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Three dimensional objects recognition & pattern recognition technique; related challenges: A review
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Spatiotemporal road scene reconstruction using superpixel-based Markov random field
TL;DR: Experiments on road detection and scene reconstruction demonstrate that the proposal outperforms state-of-the-art methods in both accuracy and computation speed, and confirm that the scene models retrieved by the proposed method have higher correctness ratio, and can support several applications.
Journal ArticleDOI
Image-Level Structure Recognition Using Image Features, Templates, and Ensemble of Classifiers
TL;DR: A novel method, using image features extracted by exploiting predefined templates, each associated with individual classifier, that achieves an 86.25% recognition accuracy on the stage dataset and a 92.58% recognition rate on the 15-scene dataset, both of which are significantly higher than the other state-of-the-art methods.
Proceedings ArticleDOI
3D Traffic Scenes Construction and Simulation based on Scene Stages
TL;DR: In this paper, a method is presented for 3D traffic Scenes Construction and Simulation based on scene stages, which proves the effectiveness of the proposed framework.
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