3D Object Reconstruction from Hand-Object Interactions

Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.Comment: International Conference on Computer Vision (ICCV) 2015, http://files.is.tue.mpg.de/dtzionas/In-Hand-Scannin

Real-time tracking of 3D elastic objects with an RGB-D sensor

This paper presents a method to track in real-time a 3D textureless object which undergoes large deformations such as elastic ones, and rigid motions, using the point cloud data provided by an RGB-D sensor. This solution is expected to be useful for enhanced manipulation of humanoid robotic systems. Our framework relies on a prior visual segmentation of the object in the image. The segmented point cloud is registered first in a rigid manner and then by non-rigidly fitting the mesh, based on the Finite Element Method to model elasticity, and on geometrical point-to-point correspondences to compute external forces exerted on the mesh. The real-time performance of the system is demonstrated on synthetic and real data involving challenging deformations and motions

Cumulative object categorization in clutter

In this paper we present an approach based on scene- or part-graphs for geometrically categorizing touching and occluded objects. We use additive RGBD feature descriptors and hashing of graph configuration parameters for describing the spatial arrangement of constituent parts. The presented experiments quantify that this method outperforms our earlier part-voting and sliding window classification. We evaluated our approach on cluttered scenes, and by using a 3D dataset containing over 15000 Kinect scans of over 100 objects which were grouped into general geometric categories. Additionally, color, geometric, and combined features were compared for categorization tasks

Self-supervised 6D Object Pose Estimation for Robot Manipulation

To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is time-consuming and expensive, enabling robots to learn in a self-supervised way is important. In this work, we introduce a robot system for self-supervised 6D object pose estimation. Starting from modules trained in simulation, our system is able to label real world images with accurate 6D object poses for self-supervised learning. In addition, the robot interacts with objects in the environment to change the object configuration by grasping or pushing objects. In this way, our system is able to continuously collect data and improve its pose estimation modules. We show that the self-supervised learning improves object segmentation and 6D pose estimation performance, and consequently enables the system to grasp objects more reliably. A video showing the experiments can be found at https://youtu.be/W1Y0Mmh1Gd8.Comment: Accepted to International Conference on Robotics and Automation (ICRA), 202

{IsMo-GAN}: {A}dversarial Learning for Monocular Non-Rigid {3D} Reconstruction

The majority of the existing methods for non-rigid 3D surface regression from monocular 2D images require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates. In this work, we present the Isometry-Aware Monocular Generative Adversarial Network (IsMo-GAN) - an approach for direct 3D reconstruction from a single image, trained for the deformation model in an adversarial manner on a light-weight synthetic dataset. IsMo-GAN reconstructs surfaces from real images under varying illumination, camera poses, textures and shading at over 250 Hz. In multiple experiments, it consistently outperforms several approaches in the reconstruction accuracy, runtime, generalisation to unknown surfaces and robustness to occlusions. In comparison to the state-of-the-art, we reduce the reconstruction error by 10-30% including the textureless case and our surfaces evince fewer artefacts qualitatively