Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and achieve a more refined mapping for unseen color images.Īdaptive Loss Balancing for Multitask Learning of Object Instance Recognition and 3D Pose Estimation real images or realistic textures for the 3D models). to learn to generate realistic features to augment the source samples), we demonstrate how our whole solution can be trained purely on augmented synthetic data, and still perform better than methods trained with domain-relevant information (e.g. As this mapping is easier to learn than the opposite one (i.e. Denoising the data and retaining only the features these recognition algorithms are familiar with, our solution greatly improves their performance. Tackling this problem from a different angle, we introduce a pipeline to map unseen target samples into the synthetic domain used to train task-specific methods. It thus became common to use available synthetic samples along domain adaptation schemes to prepare algorithms for the target domain. Keywords: Object Detection, Segmentation and Categorization, Computer Vision for Automation, Computer Vision for Other Robotic ApplicationsĪbstract: While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domain-relevant data needed for their training. Seeing Beyond Appearance – Mapping Real Images into Geometrical Domains for Unsupervised CAD-Based Recognition Furthermore, it is one of the largest public datasets for object pose estimation in general. To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for bin-picking containing sufficiently annotated data for learning-based approaches. Along with the raw data, a method for precisely annotating real-world scenes is proposed. 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. The dataset comprises both synthetic and real-world scenes. Keywords: Object Detection, Segmentation and Categorization, AI-Based Methods, Computer Vision for ManufacturingĪbstract: In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. Large-Scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking Industrial Forum II: Intelligence Technologies in Industry Keynote 12: Design and Control of BHR Humanoid Robots -By QIANG HUANG Keynote 9: Engineering Humanoids -By TAMIM ASFOUR Keynote 11: Robots with Physical Intelligence -By SANGBAE KIM Keynote 8: Snake Robots Moving on Land and Exploring the Oceans -By KRISTIN Keynote 10: Learning Human-Robot Interaction for Robot-Assisted Pedestrian Keynote 7: Living with Robots, How Far, How Close? -By GENTIANE VENTURE
0 kommentar(er)
