论文标题
感知无形：无提案的无抗圆锥形分段
Perceiving the Invisible: Proposal-Free Amodal Panoptic Segmentation
论文作者
论文摘要
Amodal Panoptic分割旨在将世界的感知与其认知理解联系起来。它需要同时预测可见场景区域的语义标签以及交通参与者实例的全部形状，包括可能被遮挡的区域。在这项工作中，我们通过首先根据其相对遮挡顺序将Amodal口罩分配给不同的层，然后在每个层上使用Amodal实例回归，同时独立学习背景语义时，在每个层上使用Amodal实例回归，在不同的层上分配了Amodal口罩，从而将该任务作为多标签和多级问题来解决此任务。我们提出了\ NET体系结构，该体系结构结合了一个共享的主链和不对称的双重编码器，该模块由几个模块组成，可促进尺度内和跨尺度的特征聚合，解码器之间的双边特征传播以及整合全球实例级别和本地像素级别和局部像素级别的封闭性推理。此外，我们提出了Amodal面膜炼油厂，该炼油厂通过明确利用未关注的实例掩码的嵌入来解决复杂的遮挡场景中的歧义。对BDD100K-APS和KITTI-360-APS数据集进行了广泛的评估表明，我们的方法在两个基准上都设定了新的最新技术。
Amodal panoptic segmentation aims to connect the perception of the world to its cognitive understanding. It entails simultaneously predicting the semantic labels of visible scene regions and the entire shape of traffic participant instances, including regions that may be occluded. In this work, we formulate a proposal-free framework that tackles this task as a multi-label and multi-class problem by first assigning the amodal masks to different layers according to their relative occlusion order and then employing amodal instance regression on each layer independently while learning background semantics. We propose the \net architecture that incorporates a shared backbone and an asymmetrical dual-decoder consisting of several modules to facilitate within-scale and cross-scale feature aggregations, bilateral feature propagation between decoders, and integration of global instance-level and local pixel-level occlusion reasoning. Further, we propose the amodal mask refiner that resolves the ambiguity in complex occlusion scenarios by explicitly leveraging the embedding of unoccluded instance masks. Extensive evaluation on the BDD100K-APS and KITTI-360-APS datasets demonstrate that our approach set the new state-of-the-art on both benchmarks.
