Object pose recovery has gained increasing attention in the computer vision field as it has
become an important problem in rapidly evolving technological areas related to autonomous …

With the availability of low-cost and compact 2.5/3D visual sensing devices, computer vision
community is experiencing a growing interest in visual scene understanding of indoor …

We introduce T-LESS, a new public dataset for estimating the 6D pose, ie translation and
rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with …

Object detection and 6D pose estimation in the crowd (scenes with multiple object instances,
severe foreground occlusions and background distractors), has become an important …

Abstract Point Pair Features is a widely used method to detect 3D objects in point clouds,
however they are prone to fail in presence of sensor noise and background clutter. We …

We propose a viewpoint invariant model for 3D human pose estimation from a single depth
image. To achieve this, our discriminative model embeds local regions into a learned …

In this paper, we introduce a new public dataset for 6D object pose estimation and instance
segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world …

In this paper, we introduce a novel single shot approach for 6D object pose estimation of
rigid objects based on depth images. For this purpose, a fully convolutional neural network …

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 …

Intra-class variations, distribution shifts among source and target domains are the major
challenges of category-level tasks. In this study, we address category-level full 6D object …