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.
We propose a novel model-based method for estimating and tracking the six-degrees-of-freedom (6DOF) pose of rigid objects of arbitrary shapes in real-time. By combining dense motion and stereo cues with sparse key point correspondences, and by feeding back information from the model to the cue extraction level, the method is both highly accurate and robust to noise and occlusions. A tight integration of the graphical and computational capability of Graphics Processing Units (GPUs) results in pose updates at frame rates exceeding 60 Hz. Since a benchmark dataset that enables the evaluation of stereo-vision-based pose estimators in complex scenarios is currently missing in the literature, we have introduced a novel synthetic benchmark dataset with varying objects, background motion, noise and occlusions. Using this dataset and a novel evaluation methodology, we show that the proposed method greatly outperforms state-of-the-art methods. Finally, we demonstrate excellent performance on challenging real-world sequences involving object manipulation. This paper proposes an effective algorithm for recognizing objects and accurately estimating their 6DOF pose in scenes acquired by a RGB-D sensor. The proposed method is based on a combination of different recognition pipelines, each exploiting the data in a diverse manner and generating object hypotheses that are ultimately fused together in an Hypothesis Verification stage that globally enforces geometrical consistency between model hypotheses and the scene. Such a scheme boosts the overall recognition performance as it enhances the strength of the different recognition pipelines while diminishing the impact of their specific weaknesses. The proposed method outperforms the state-of-the-art on two challenging benchmark datasets for object recognition comprising 35 object models and, respectively, 176 and 353 scenes. In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor - the Fast Point Feature Histograms, and discriminative graphical models. To build informative and robust 3D feature point representations, our descriptors encode the underlying surface geometry around a point p using multi-value histograms. This highly dimensional feature space copes well with noisy sensor data and is not dependent on pose or sampling density. By defining classes of 3D geometric surfaces and making use of contextual information using Conditional Random Fields (CRFs), our system is able to successfully segment and label 3D point clouds, based on the type of surfaces the points are lying on. We validate and demonstrate the method's efficiency by comparing it against similar initiatives as well as present results for table setting datasets acquired in indoor environments. 3D object recognition from local features is robust to occlusions and clutter. However, local features must be extracted from a small set of feature rich keypoints to avoid computational complexity and ambiguous features. We present an algorithm for the detection of such keypoints on 3D models and partial views of objects. The keypoints are highly repeatable between partial views of an object and its complete 3D model. We also propose a quality measure to rank the keypoints and select the best ones for extracting local features. Keypoints are identified at locations where a unique local 3D coordinate basis can be derived from the underlying surface in order to extract invariant features. We also propose an automatic scale selection technique for extracting multi-scale and scale invariant features to match objects at different unknown scales. Features are projected to a PCA subspace and matched to find correspondences between a database and query object. Each pair of matching features gives a transformation that aligns the query and database object. These transformations are clustered and the biggest cluster is used to identify the query object. Experiments on a public database revealed that the proposed quality measure relates correctly to the repeatability of keypoints and the multi-scale features have a recognition rate of over 95 for up to 80 occluded objects.  In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.  This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance. This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.  There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to individual training images. This paper presents a method for combining multiple images of a 3D object into a single model representation. This provides for recognition of 3D objects from any viewpoint, the generalization of models to non-rigid changes, and improved robustness through the combination of features acquired under a range of imaging conditions. The decision of whether to cluster a training image into an existing view representation or to treat it as a new view is based on the geometric accuracy of the match to previous model views. A new probabilistic model is developed to reduce the false positive matches that would otherwise arise due to loosened geometric constraints on matching 3D and non-rigid models. A system has been developed based on these approaches that is able to robustly recognize 3D objects in cluttered natural images in sub-second times.