Dataset of Industrial Metal Objects

Abstract

We present a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our dataset contains both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data is obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions and lighting conditions. This results in over 30,000 images, accurately labelled using a new public tool. Synthetic data is obtained by carefully simulating realworld conditions and varying them in a controlled and realistic way. This leads to over 500,000 synthetic images. The close correspondence between synthetic and real-world data, and controlled variations, will facilitate sim-toreal research. Our dataset's size and challenging nature will facilitate research on various computer vision tasks involving reflective materials. The dataset and accompanying resources are made available on the project website https://pderoovere.github.io/dimo.

CenDerNet: Center and Curvature Representations for Render-and-Compare 6D Pose Estimation

“…We evaluate our method on the 6D localization task as defined in the BOP challenge [11] on DIMO [4] and T-LESS [9], two industry-relevant datasets. On DIMO, we show our method significantly outperforms PVNet [20], a strong single-view baseline.…”

“…Manufacturing use cases present unique challenges. Many industrial objects are reflective and textureless, with scratches or saw patterns affecting their appearance [32,4]. Parts are often stacked in dense compositions, with many occlusions.…”

“…Dataset The dataset of industrial metal objects (DIMO) [4] reflects real-world industrial conditions. Scenes consist of symmetric, textureless and highly reflective metal objects, stacked in dense compositions.…”

Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities - A Framework and Dataset Creation Approach

“…The creation of industry related datasets is the topic of this subsection. Obtaining and marking such datasets in an industrial environment can be difficult due to factors such as it being time-consuming, susceptible to human mistakes, and constrained by various privacy and security regulations [34,43,44]. Therefore, using a semi-or fully-automated pipeline for the dataset creation should be considered.…”

“…Synthetic-based industrial object datasets are, e.g., created in the research work of [43,44]. The authors of [43] develop both real-world and synthetic data of industrial metal or reflective objects that are arranged as multi-view RGB images with 6D object pose labels. The real-world objects dataset contains 600 scenes with 31, 200 RGB images and the synthetic data provides 42, 600 synthetic scenes containing 553, 800 images.…”