We propose the task of Panoptic Scene Completion (PSC) which extends the
recently popular Semantic Scene Completion (SSC) task with instance-level
information to produce a richer understanding of the 3D scene. Our PSC proposal
utilizes a hybrid mask-based technique on the non-empty voxels from sparse
multi-scale completions. Whereas the SSC literature overlooks uncertainty which
is critical for robotics applications, we instead propose an efficient
ensembling to estimate both voxel-wise and instance-wise uncertainties along
PSC. This is achieved by building on a multi-input multi-output (MIMO)
strategy, while improving performance and yielding better uncertainty for
little additional compute. Additionally, we introduce a technique to aggregate
permutation-invariant mask predictions. Our experiments demonstrate that our
method surpasses all baselines in both Panoptic Scene Completion and
uncertainty estimation on three large-scale autonomous driving datasets. Our
code and data are available at this https URL .

我们提出了全景场景完成（PSC）的任务，它扩展了最近流行的语义场景完成（SSC）任务，通过实例级别的信息产生对三维场景的更丰富理解。我们的 PSC 提议利用基于蒙版的混合技术处理来自稀疏多尺度完成的非空体素。与 SSC 文献忽略机器人应用中至关重要的不确定性不同，我们提出了一种有效的集成方法来估计体素和实例的不确定性，同时提高性能并获得更好的不确定性，且计算成本较低。此外，我们引入了一种聚合排列不变蒙版预测的技术。我们的实验证明，我们的方法在全景场景完成和不确定性估计方面优于所有基线算法，且在三个大规模自动驾驶数据集上进行了测试。我们的代码和数据可在此 https URL 中获取。

PaSCo：具有不确定性感知的城市 3D 全景场景补全

PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness

Most teacher-student frameworks based on knowledge distillation (KD) depend
on a strong congruent constraint on instance level. However, they usually
ignore the correlation between multiple instances, which is also valuable for
knowledge transfer. In this work, we propose a new framework named correlation
congruence for knowledge distillation (CCKD), which transfers not only the
instance-level information, but also the correlation between instances.
Furthermore, a generalized kernel method based on Taylor series expansion is
proposed to better capture the correlation between instances. Empirical
experiments and ablation studies on image classification tasks (including
CIFAR-100, ImageNet-1K) and metric learning tasks (including ReID and Face
Recognition) show that the proposed CCKD substantially outperforms the original
KD and achieves state-of-the-art accuracy compared with other SOTA KD-based
methods. The CCKD can be easily deployed in the majority of the teacher-student
framework such as KD and hint-based learning methods.

本文提出了一种新的基于相关性的一致性知识蒸馏框架 (CCKD)，通过引入泰勒级数展开的广义核方法捕获实例间的相关性，并在图像分类任务和度量学习任务中实现了最优结果。