Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
High-Level Fusion of Depth and Intensity for Pedestrian Classification
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple depth cameras and projectors for interactions on, above and between surfaces
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Monocular 3D scene modeling and inference: understanding multi-object traffic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Multiple viewpoint recognition and localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Proceedings of the 24th annual ACM symposium on User interface software and technology
SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Conditional Random Fields for multi-camera object detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Script data for attribute-based recognition of composite activities
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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Categorizing and localizing multiple objects in 3D space is a challenging but essential task for many robotics and assisted living applications. While RGB cameras as well as depth information have been widely explored in computer vision there is surprisingly little recent work combining multiple cameras and depth information. Given the recent emergence of consumer depth cameras such as Kinect we explore how multiple cameras and active depth sensors can be used to tackle the challenge of 3D object detection. More specifically we generate point clouds from the depth information of multiple registered cameras and use the VFH descriptor [20] to describe them. For color images we employ the DPM [3] and combine both approaches with a simple voting approach across multiple cameras. On the large RGB-D dataset [12] we show improved performance for object classification on multi-camera point clouds and object detection on color images, respectively. To evaluate the benefit of joining color and depth information of multiple cameras, we recorded a novel dataset with four Kinects showing significant improvements over a DPM baseline for 9 object classes aggregated in challenging scenes. In contrast to related datasets our dataset provides color and depth information recorded with multiple Kinects and requires localizing and categorizing multiple objects in 3D space. In order to foster research in this field, the dataset, including annotations, is available on our web page.