Pipelines to recognize 3D objects despite clutter and occlusions usually end up with a final verification stage whereby recognition hypotheses are validated or dismissed based on how well they explain sensor measurements. Unlike previous work, we propose a Global Hypothesis Verification (GHV) approach which regards all hypotheses jointly so as to account for mutual interactions. GHV provides a principled framework to tackle the complexity of our visual world by leveraging on a plurality of recognition paradigms and cues. Accordingly, we present a 3D object recognition pipeline deploying both global and local 3D features as well as shape and color. Thereby, and facilitated by the robustness of the verification process, diverse object hypotheses can be gathered and weak hypotheses need not be suppressed too early to trade sensitivity for specificity. Experiments demonstrate the effectiveness of our proposal, which significantly improves over the state-of-art and attains ideal performance (no false negatives, no false positives) on three out of the six most relevant and challenging benchmark datasets.

We propose a method for recognizing rigid object instances in RGB-D point clouds by accumulating low-level information from keypoint correspondences over multiple observations. Compared to existing multi-view approaches, we make fewer assumptions on the recognition problem, dealing with cluttered and partially dynamic environments as well as covering a wide range of objects. Evaluation on the publicly available TUW and Willow datasets showed that our method achieves state-of-the-art recognition performance for challenging sequences of static environments and a significant improvement for environments partially changing during the observation.

This work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor. A major advantage of the method is that unlike other modelling tools, our reconstruction pipeline allows the user to acquire a full 3D model of the object. This is achieved by acquiring several partial 3D models in different sessions-each individual session presenting the object of interest in different configurations that reveal occluded parts of the object - that are automatically merged together to reconstruct a full 3D model. In addition, the 3D models acquired by our system can be directly used by state-of-the-art object instance recognition and object tracking modules, providing object-perception capabilities to complex applications requiring these functionalities (e.g. human-object interaction analysis, robot grasping, etc.). The system does not impose constraints in the appearance of objects (textured, untextured) nor in the modelling setup (moving camera with static object or turn-table setups with static camera). The proposed reconstruction system has been used to model a large number of objects resulting in metrically accurate and visually appealing 3D models.

Aiming at reducing the labour intensity associated with the acquisition of ground truth annotations for object instance recognition datasets, this paper discusses a novel multi-view recognition method to automate the annotation (object instances and associated poses) of individual images in multi-view RGB-D datasets. In combination with recent single-view object recognition techniques, the supplementary information provided by multiple vantage points results in a rich and integrated representation of the environment, in the form of a 3D reconstructed scene as well as object hypotheses therein. We argue that such a representation facilitates improved recognition to an extent that the recovered results, obtained by means of a suitable 3D hypotheses verification stage, closely resemble the ground truth of the scene under consideration. On two large datasets, totalling more than 3500 object instances, our method yields 99.1% and 93.2% correct automatic annotations. These results corroborate our approach for the task at hand.

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.

The ability to perceive possible interactions with the environment is a key capability of task-guided robotic agents. An important subset of possible interactions depends solely on the objects of interest and their position and orientation in the scene. We call these object-based interactions 0-order affordances and divide them among non-hidden and hidden whether the current configuration of an object in the scene renders its affordance directly usable or not. Conversely to other works, we propose that detecting affordances that are not directly perceivable increase the usefulness of robotic agents with manipulation capabilities, so that by appropriate manipulation they can modify the object configuration until the seeked affordance becomes available. In this paper we show how 0-order affordances depending on the geometry of the objects and their pose can be learned using a supervised learning strategy on 3D mesh representations of the objects allowing the use of the whole object geometry. Moreover, we show how the learned affordances can be detected in real scenes obtained with a low-cost depth sensor like the Microsoft Kinect through object recognition and 6D0F pose estimation and present results for both learning on meshes and detection on real scenes to demonstrate the practical application of the presented approach.

3D object and object class recognition gained momentum with the arrival of low-cost RGB-D sensors and enables robotics tasks not feasible years ago. Scaling object class recognition to hundreds of classes still requires extensive time and many objects for learning. To overcome the training issue, we introduce a methodology for learning 3D descriptors from synthetic CAD-models and classification of never-before-seen objects at the first glance, where classification rates and speed are suited for robotics tasks. We provide this in 3DNet (3d-net.org), a free resource for object class recognition and 6DOF pose estimation from point cloud data. 3DNet provides a large-scale hierarchical CAD-model databases with increasing numbers of classes and difficulty with 10, 50, 100 and 200 object classes together with evaluation datasets that contain thousands of scenes captured with a RGB-D sensor. 3DNet further provides an open-source framework based on the Point Cloud Library (PCL) for testing new descriptors and benchmarking of state-of-the-art descriptors together with pose estimation procedures to enable robotics tasks such as search and grasping.