Real-time texturing for 6D object instance detection from RGB Images
This work addresses the challenge of color information absence in synthetic data for object detection, offering an incremental improvement for computer vision applications.
The paper tackles the problem of object detection when training on synthetic CAD data lacking color cues by presenting a real-time method for generating texture-maps from image sequences, which significantly improves detection rates using the LINEMOD detector on RGB images and enables differentiation of object instances by surface color.
For objected detection, the availability of color cues strongly influences detection rates and is even a prerequisite for many methods. However, when training on synthetic CAD data, this information is not available. We therefore present a method for generating a texture-map from image sequences in real-time. The method relies on 6 degree-of-freedom poses and a 3D-model being available. In contrast to previous works this allows interleaving detection and texturing for upgrading the detector on-the-fly. Our evaluation shows that the acquired texture-map significantly improves detection rates using the LINEMOD detector on RGB images only. Additionally, we use the texture-map to differentiate instances of the same object by surface color.