Recovering Surface Layout from an Image
International Journal of Computer Vision
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Make3D: depth perception from a single still image
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Shape-from-recognition: Recognition enables meta-data transfer
Computer Vision and Image Understanding
3D Object Mapping by Integrating Stereo SLAM and Object Segmentation Using Edge Points
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
International Journal of Computer Vision
A close-form iterative algorithm for depth inferring from a single image
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Thinking inside the box: using appearance models and context based on room geometry
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Simultaneous non-gaussian data clustering, feature selection and outliers rejection
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Toward coherent object detection and scene layout understanding
Image and Vision Computing
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Semantic structure from motion: a novel framework for joint object recognition and 3d reconstruction
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
A generic model to compose vision modules for holistic scene understanding
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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We develop an integrated, probabilistic model for the appearance and three-dimensional geometry of cluttered scenes. Object categories are modeled via distributions over the 3D location and appearance of visual features. Uncertainty in the number of object instances depicted in a particular image is then achieved via a transformed Dirichlet process. In contrast with image-based approaches to object recognition, we model scale variations as the perspective projection of objects in different 3D poses. To calibrate the underlying geometry, we incorporate binocular stereo images into the training process. A robust likelihood model accounts for outliers in matched stereo features, allowing effective learning of 3D object structure from partial 2D segmentations. Applied to a dataset of office scenes, our model detects objects at multiple scales via a coarse reconstruction of the corresponding 3D geometry.