ESKO6d  A Binocular and RGBD Dataset of Stored Kitchen Objects with 6d Poses * We present a new dataset with the goal of advancing the stateoftheart in object pose estimation especially for stored porcelain and glass crockery in kitchen scenes. Specifically the ESKO 6d (EASE Stored Kitchen Objects with 6d poses) dataset features textureless, glossy or glassy ordinary used objects which were naturally stored in a cupboard, drawer or dishwasher. There is a large degree of occlusion being the specific challenge in these scenes. Each scene was recorded in video sequences by two cameras (RGBD (Kinect) and binocular) within multiple setup stages. The dataset contains an RGBD image or binocular RGB image plus stereomatched depth image as well as 6d pose ground truth and instance segmentation. Our dataset contains twelve stored object scenes with a combined amount of 47 video sequences captured by each camera, resulting in over 17k annotated Kinect images and more than 42k annotated stereo images showing around 50 different objects. The ground truth annotation is precise to 3. 5mm ADD (details see paper). The dataset can be accessed under http:www.informatik.unibremen.deesko6ddataset. Besides the concrete dataset we propose a method of ground truth pose measurement based on an external 3d tracking system that allows on the one hand to precisely measure the objectxe2x80x99s pose inside a tight packed storage and on the other hand to obtain the object pose in several images with just one manual measurement.

Largescale 6D Object Pose Estimation Dataset for Industrial BinPicking In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial binpicking. The dataset comprises both synthetic and realworld scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a visibility score, and a segmentation mask for each object are provided. Along with the raw data, a method for precisely annotating realworld scenes is proposed.To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for binpicking containing sufficiently annotated data for learningbased approaches. Furthermore, it is one of the largest public datasets for object pose estimation in general. The dataset is publicly available at http:www.binpicking.aien dataset.html.