  Scene analysis is essential for enabling autonomous systems, such as mobile
robots, to operate in real-world environments. However, obtaining a
comprehensive understanding of the scene requires solving multiple tasks, such
as panoptic segmentation, instance orientation estimation, and scene
classification. Solving these tasks given limited computing and battery
capabilities on mobile platforms is challenging. To address this challenge, we
introduce an efficient multi-task scene analysis approach, called EMSAFormer,
that uses an RGB-D Transformer-based encoder to simultaneously perform the
aforementioned tasks. Our approach builds upon the previously published
EMSANet. However, we show that the dual CNN-based encoder of EMSANet can be
replaced with a single Transformer-based encoder. To achieve this, we
investigate how information from both RGB and depth data can be effectively
incorporated in a single encoder. To accelerate inference on robotic hardware,
we provide a custom NVIDIA TensorRT extension enabling highly optimization for
our EMSAFormer approach. Through extensive experiments on the commonly used
indoor datasets NYUv2, SUNRGB-D, and ScanNet, we show that our approach
achieves state-of-the-art performance while still enabling inference with up to
39.1 FPS on an NVIDIA Jetson AGX Orin 32 GB.
