Robots operating in the real world are expected to detect, classify, segment, and estimate the pose of objects to accomplish their task. Modern approaches using deep learning not only require large volumes of data but also pixel-accurate annotations in order to evaluate the performance and therefore safety of these algorithms. At present, publicly available tools for annotating data are scarce and those that are available rely on depth sensors, which excludes their use for transparent, metallic, and general non-Lambertian objects. To address this issue, we present a novel method for creating valuable datasets that can be used in these more difficult cases. Our key contribution is a purely RGB-based scene-level annotation approach that uses a neural radiance field-based method to automatically align objects. A set of user studies demonstrates the accuracy and speed of our approach over a purely manual or depth sensor assisted pipeline. We provide an open-source implementation of each component and a ROS-based recorder for capturing data with a eye-in-hand robot system. Code will be made available at https://github.com/markus-suchi/3D-DAT.

Developing robot perception systems for recognizing objects in the real world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms. This paper presents the EasyLabel tool for easily acquiring high-quality ground truth annotation of objects at pixel-level in densely cluttered scenes. In a semi-automatic process, complex scenes are incrementally built and EasyLabel exploits depth changes to extract precise object masks at each step. We use this tool to generate the Object Cluttered Indoor Dataset (OCID) that captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. OCID is used to perform a systematic comparison of existing object segmentation methods. The baseline comparison supports the need for pixel- and object-wise annotation to progress robot vision towards realistic applications. This insight reveals the usefulness of EasyLabel and OCID to better understand the challenges that robots face in the real world.