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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(More)
Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization(More)
This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal stems from a taxonomy of existing methods which highlights two major approaches, referred to as Signatures and Histograms, inherently emphasizing descriptiveness and robustness respectively. We formulate a comprehensive proposal which encompasses a repeatable local(More)
The use of robust feature descriptors is now key for many 3D tasks such as 3D object recognition and surface alignment. Many descriptors have been proposed in literature which are based on a non-unique local Reference Frame and hence require the computation of multiple descriptions at each feature points. In this paper we show how to deploy a unique local(More)
We propose a novel approach for verifying model hypotheses in cluttered and heavily occluded 3D scenes. Instead of verifying one hypothesis at a time, as done by most state-of-the-art 3D object recognition methods, we determine object and pose instances according to a global optimization stage based on a cost function which encompasses geometrical cues.(More)
This paper addresses the problem of estimating the depth map of a scene given a single RGB image. To model the ambiguous mapping between monocular images and depth maps, we leverage on deep learning capabilities and present a fully convolutional architecture encompassing residual learning. The proposed model is deeper than the current state of the art, but(More)
In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the focus of intense research activity, due on the one hand to the increasing diffusion of low-cost 3D sensors, on the other to the growing importance of applications such as 3D shape retrieval and 3D object recognition. This work aims at contributing to the(More)
Recent research activity on stereo matching has proved the efficacy of local approaches based on advanced cost aggregation strategies in accurately retrieving 3D information. However, accuracy is typically achieved at expense of computational efficiency, with best methods being far from meeting real-time requirements. On the other side, basic real-time(More)
We propose a novel method to estimate a unique and re-peatable reference frame in the context of 3D object recognition from a single viewpoint based on global descriptors. We show that the ability of defining a robust reference frame on both model and scene views allows creating descriptive global representations of the object view, with the beneficial(More)
Object detection in images withstanding significant clutter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and distinctive representation of groups of neighboring line segments aggregated over limited spatial supports and invariant to(More)