Depth-aware panoptic segmentation (DPS) combines image segmentation and monocular depth estimation in a single model to achieve semantic and geometry perception simultaneously. DPS task has important applications in the robot area but the previous DPS models are too heavy to be applied. Thus, we propose EfficientDPS, an efficient, end-to-end, and unified model for DPS. In our method, query features extracted with convolution networks are used to represent things/stuff. In this way, different vision tasks such as classification, segmentation, and depth estimation can be realized in a unified manner, leading to a compact and efficient model. EfficientDPS can be trained and tested in an end-to-end manner via bipartite matching and complex post-process is not needed at inference. To enhance the supervision signal, group query representation is proposed, leading to better performance without affecting the inference speed. Extensive experiments on Cityscapes-DPS and SemKITTI-DPS show that EfficientDPS can achieve the best trade-off between speed and accuracy than the state-of-the-art methods.

Abstract Most anchor‐based detectors use intersection over union (IoU) to assign targets to anchors during training. However, IoU did not pay enough attention to the proximity of the anchor's centre to the centre of the truth box, resulting in two issues: (1) the most slender objects were given just one anchor, resulting in insufficient supervision information for slender objects during training; (2) IoU cannot accurately represent the degree of alignment between the feature's receptive field at the anchor's centre and the object. As a result, some features with good alignment degrees are missing, while others with poor alignment degrees are used, reducing the model's localisation accuracy. To address these issues, we first created a Gaussian Guided IoU (GGIoU), which prioritises the proximity of the anchor's centre to the truth box's centre. We then proposed GGIoU‐balanced learning methods, including GGIoU‐guided assignment strategy and GGIoU‐balanced localisation loss. This method can assign multiple anchors to each slender object, favouring features that are well‐aligned with the objects during the training process. A large number of experiments show that GGIoU‐balanced learning can solve the aforementioned problems and significantly improve the detection model's performance.