  Although instance-aware perception is a key prerequisite for many autonomous
robotic applications, most of the methods only partially solve the problem by
focusing solely on known object categories. However, for robots interacting in
dynamic and cluttered environments, this is not realistic and severely limits
the range of potential applications. Therefore, we propose a novel object
instance segmentation approach that does not require any semantic or geometric
information of the objects beforehand. In contrast to existing works, we do not
explicitly use depth data as input, but rely on the insight that slight
viewpoint changes, which for example are provided by stereo image pairs, are
often sufficient to determine object boundaries and thus to segment objects.
Focusing on the versatility of stereo sensors, we employ a transformer-based
architecture that maps directly from the pair of input images to the object
instances. This has the major advantage that instead of a noisy, and
potentially incomplete depth map as an input, on which the segmentation is
computed, we use the original image pair to infer the object instances and a
dense depth map. In experiments in several different application domains, we
show that our Instance Stereo Transformer (INSTR) algorithm outperforms current
state-of-the-art methods that are based on depth maps. Training code and
pretrained models will be made available.
