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PaSCo
Urban 3D Panoptic Scene Completion with
Uncertainty Awareness
CVPR 2024 Oral, Best Paper Award Candidate
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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.
Demo
Overview of our method
Panoptic Scene Completion Comparison
Uncertainty Estimation Comparison
Robusness Evaluation on Robo3D
Citation
@InProceedings{cao2024pasco,
title={PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness},
author={Anh-Quan Cao and Angela Dai and Raoul de Charette},
year={2024},
booktitle = {CVPR},
}