Rigas Kouskouridas

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In this paper we propose a novel framework, Latent-Class Hough Forests, for 3D object detection and pose estimation in heavily cluttered and oc-cluded scenes. Firstly, we adapt the state-of-the-art template matching feature, LINEMOD [14], into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split(More)
Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to(More)
In this paper we tackle the problem of estimating the 3D pose of object instances, using convolutional neural networks. State of the art methods usually solve the challenging problem of regression in angle space indirectly, focusing on learning discriminative features that are later fed into a separate architecture for 3D pose estimation. In contrast, we(More)
In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the art template matching feature into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. We train with positive(More)
The ACROBOTER project aims to develop a radically new locomotion technology that can effectively be used in a home and/or in a workplace environment for manipulating small objects autonomously. It extends the workspace of existing indoor service robots in the vertical direction, whilst its novel structure allows covering the whole volume of a room. For the(More)
State-of-the-art techniques for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in the 3D space. To reduce this dependency, we introduce a novel architecture called Iterative Hough forest with Histogram of Control Points that is capable of estimating occluded and cluttered objects' 6D pose given a candidate 2D(More)