论文标题
monodvps：一种自我监督的单眼深度估计方法
MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation
论文作者
论文摘要
深度感知的视频全景分割解决了从视频序列恢复圆锥体3D点云的反向投影问题，其中3D点通过语义类增强，并在时间上保持一致的实例标识符。我们提出了一个具有多任务网络的新颖解决方案，该解决方案可以执行单眼深度估计和视频泛型分割。自从获得深度和图像分割的地面真实标签的成本相对较高，我们利用了未标记的视频序列的功能，具有自我监督的单眼深度估计，并从伪标签中学习半避风式学习，以进行视频综合分段。为了进一步改善深度预测，我们引入了全景引导的深度损失和一种新颖的泛膜掩蔽方案，以避免损坏训练信号。对CityScapes-DVP和Semkitti-DVPS数据集进行的广泛实验表明，我们提出的改进的模型可实现竞争成果和快速推理速度。
Depth-aware video panoptic segmentation tackles the inverse projection problem of restoring panoptic 3D point clouds from video sequences, where the 3D points are augmented with semantic classes and temporally consistent instance identifiers. We propose a novel solution with a multi-task network that performs monocular depth estimation and video panoptic segmentation. Since acquiring ground truth labels for both depth and image segmentation has a relatively large cost, we leverage the power of unlabeled video sequences with self-supervised monocular depth estimation and semi-supervised learning from pseudo-labels for video panoptic segmentation. To further improve the depth prediction, we introduce panoptic-guided depth losses and a novel panoptic masking scheme for moving objects to avoid corrupting the training signal. Extensive experiments on the Cityscapes-DVPS and SemKITTI-DVPS datasets demonstrate that our model with the proposed improvements achieves competitive results and fast inference speed.
