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。