The diagnosis of unruptured intracranial aneurysms from time-of-flight Magnetic Resonance Angiography (TOF-MRA) images is a challenging clinical problem that is extremely difficult to automate. We propose to go beyond the mere detection of each aneurysm and also estimate its size and the orientation of its main axis for an immediate visualization in appropriate reformatted cut planes. To address this issue, and inspired by the idea behind YOLO architecture, a novel one-stage deep learning approach is described to simultaneously estimate the localization, size and orientation of each aneurysm in 3D images. It combines fast and approximate annotation, data sampling and generation to tackle the class imbalance problem, and a cosine similarity loss to optimize the orientation. We evaluate our approach on two large datasets containing 416 patients with 317 aneurysms using a 5-fold cross-validation scheme. Our method achieves a median localization error of 0.48 mm and a median 3D orientation error of 12.27 degrees, demonstrating an accurate localization of aneurysms and an orientation estimation that comply with clinical practice. Further evaluation is performed in a more classical detection setting to compare with state-of-the-art nnDetecton and nnUNet methods. Competitive performance is reported with an average precision of 76.60%, a sensitivity score of 82.93%, and 0.44 false positives per case. Code and annotations are publicly available at https://gitlab.inria.fr/yassis/DeepAnePose.