Rapid Estimation of Optical Properties for Simulation-Based Evaluation of Pose Estimation Performance

A growing trend in computer vision is the use of synthetic images for the evaluation of computer vision algorithms such as 3D pose estimation. This is partly due to the availability of high-quality render engines, which provide highly realistic synthetic images. However, the realism of the rendered images, and thus the reliability of the evaluations, strongly depends on how accurately the scenes are modeled and it requires considerable time and knowledge to do the modeling manually. Automating the modeling process is therefore crucial for making the rendering of photo-realistic synthetic images accessible to the wider robotics community. We present a method for automatically modeling object and light properties for rigid, opaque plastic objects commonly found in industry. Our method relies on recordings of the environment captured with a consumer $360^{\mathrm{o}}$ camera to model the light, and on analysis-by-synthesis to estimate the optical properties of the objects. We show that the synthetic images rendered based on our automatic modeling method can be used to predict the overall performance of a monocular 3D pose estimation algorithm.