We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder that has been trained on a large collection of random local patches. During testing, scene patch descriptors are matched against a database of synthetic model view patches and cast 6D object votes which are subsequently filtered to refined hypotheses. We evaluate on three datasets to show that our method generalizes well to previously unseen input data, delivers robust detection results that compete with and surpass the state-of-the-art while being scalable in the number of objects.
On the other hand, template-based approaches are often very robust to clutter but scale linearly with the number of models. LineMOD @cite_6 performed robust 3D object detection by matching templates extracted from rendered views of 3D models and embedding quantized image contours and normal orientations. Successively, @cite_32 optimized the matching via a cascaded classification scheme, achieving a run-time increase by a factor of 10. Improvements in efficiency are also achieved by the two-stage cascaded detection method in @cite_9 and by the hashing matching approach tailored to LineMOD templates proposed in @cite_23 . Other recent approaches @cite_24 @cite_25 @cite_21 build discriminative models based on such representations using SVM or boosting applied to training data.
@cite_9 @cite_21 @cite_32 @cite_6 @cite_24 @cite_23 @cite_25
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In this paper we present a novel template-based approach for fast object detection. In particular we investigate the use of Dominant Orientation Templates (DOT), a binary template representation introduced by , as a means for fast detection of objects even if textureless. During training, we learn a binary mask for each template that allows to remove background clutter while at the same time including relevant context information. These mask templates then serve as weak classifiers in an Adaboost framework. We demonstrate our method on detection of shape-oriented object classes as well as multiview vehicle detection. We obtain a fast yet highly accurate method for category level detection that compares favorably to other more complicated yet much slower approaches. We further show how to efficiently transfer meta-data using the top most similar activated templates. Finally, we propose an optimization scheme for detection of specific objects using our proposed masks trained by the SVM, resulting in an increment of up to 17 in performance of the DOT method, without sacrificing testing speed and it is able to run the training on real time. We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13 for SVM and 7 for convolutional nets. On a segmentation recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16 7 error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second. We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets. In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D LINEMOD representation introduced recently by , yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.