2021/12/30 by Tomas Hodan, Tomáš Hodaň, Hodan, Tomas
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graphics (cs.GR) #Image Processing and 3D Reconstruction #Image and Object Detection Techniques #Machine Learning (cs.LG) #Robotics (cs.RO) #cs.AI #cs.CV #cs.GR #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.2112.15075
Tomas Hodan's PhD thesis defended on July 7, 2021. Supervisor: Prof. Jiri Matas. Reviewers: Prof. Vincent Lepetit, Prof. Markus Vincze, Dr. Slobodan Ilic. A recording of the defense: https://youtu.be/WAQmubEXCRM
arxiv created 2021/12/30 · openalex publication_date 2021/12/30 · arxiv updated 2022/01/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this thesis, we address the problem of estimating the 6D pose of rigid objects from a single RGB or RGB-D input image, assuming that 3D models of the objects are available. This problem is of great importance to many application fields such as robotic manipulation, augmented reality, and autonomous driving. First, we propose EPOS, a method for 6D object pose estimation from an RGB image. The key idea is to represent an object by compact surface fragments and predict the probability distribution of corresponding fragments at each pixel of the input image by a neural network. Each pixel is linked with a data-dependent number of fragments, which allows systematic handling of symmetries, and the 6D poses are estimated from the links by a RANSAC-based fitting method. EPOS outperformed all RGB and most RGB-D and D methods on several standard datasets. Second, we present HashMatch, an RGB-D method that slides a window over the input image and searches for a match against templates, which are pre-generated by rendering 3D object models in different orientations. The method applies a cascade of evaluation stages to each window location, which avoids exhaustive matching against all templates. Third, we propose ObjectSynth, an approach to synthesize photorealistic images of 3D object models for training methods based on neural networks. The images yield substantial improvements compared to commonly used images of objects rendered on top of random photographs. Fourth, we introduce T-LESS, the first dataset for 6D object pose estimation that includes 3D models and RGB-D images of industry-relevant objects. Fifth, we define BOP, a benchmark that captures the status quo in the field. BOP comprises eleven datasets in a unified format, an evaluation methodology, an online evaluation system, and public challenges held at international workshops organized at the ICCV and ECCV conferences.