We present TICaM, a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. Our dataset addresses the deficiencies of currently available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and provided annotations; all at the same time. We record an exhaustive list of actions performed while driving and provide for them multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection, instance and semantic segmentation as well as activity annotations for RGB frames. Additional to real recordings, we provide a synthetic dataset of in-car cabin images with same multi-modality of images and annotations, providing a unique and extremely beneficial combination of synthetic and real data for effectively training cabin monitoring systems and evaluating domain adaptation approaches. The dataset is available at https://vizta-tof.kl.dfki.de/.
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