PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness

• URL: http://arxiv.org/abs/2312.02158v2
• Date: Sat, 25 May 2024 11:20:53 GMT
• Title: PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness
• Authors: Anh-Quan Cao, Angela Dai, Raoul de Charette,
• Abstract summary: Panoptic Scene Completion (PSC) task extends the popular Semantic Scene Completion (SSC) task with instance-level information. Our PSC proposal utilizes a hybrid mask-based technique on the non-empty voxels from sparse multi-scale completions. Our method surpasses all baselines in both Panoptic Scene Completion and uncertainty estimation on three large-scale autonomous driving datasets.
• Score: 38.802781781863196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://astra-vision.github.io/PaSCo .

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