This paper presents a general method for object pose estimation from RGB-D camera data for robot manipulation tasks. We fine-tune off-the-shelf image detection models to recognize certain objects in color images then combine the result with point cloud information to estimate 3D object positions in a task-agnostic approach. By utilizing prior information about our manipulation task, we further estimate object orientations using additional heuristics. We demonstrate our approach and evaluate its performance on an electronic task board and release our adaptable and easy-to-integrate implementation as a re-usable software module under https://github.com/eurobin-wp1/tum-tb-perception .