An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving

• URL: http://arxiv.org/abs/2603.27238v1
• Date: Sat, 28 Mar 2026 11:22:31 GMT
• Title: An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving
• Abstract summary: This paper presents an instance-centric benchmark for the 3D panoptic occupancy prediction task.<n>Specifically, we introduce ADMesh, the first unified 3D mesh library tailored for autonomous driving.<n>We construct CarlaOcc, a large-scale, physically consistent panoptic occupancy dataset.
• Score: 25.37985701473846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Panoptic occupancy prediction aims to jointly infer voxel-wise semantics and instance identities within a unified 3D scene representation. Nevertheless, progress in this field remains constrained by the absence of high-quality 3D mesh resources, instance-level annotations, and physically consistent occupancy datasets. Existing benchmarks typically provide incomplete and low-resolution geometry without instance-level annotations, limiting the development of models capable of achieving precise geometric reconstruction, reliable occlusion reasoning, and holistic 3D understanding. To address these challenges, this paper presents an instance-centric benchmark for the 3D panoptic occupancy prediction task. Specifically, we introduce ADMesh, the first unified 3D mesh library tailored for autonomous driving, which integrates over 15K high-quality 3D models with diverse textures and rich semantic annotations. Building upon ADMesh, we further construct CarlaOcc, a large-scale, physically consistent panoptic occupancy dataset generated using the CARLA simulator. This dataset contains over 100K frames with fine-grained, instance-level occupancy ground truth at voxel resolutions as fine as 0.05 m. Furthermore, standardized evaluation metrics are introduced to quantify the quality of existing occupancy datasets. Finally, a systematic benchmark of representative models is established on the proposed dataset, which provides a unified platform for fair comparison and reproducible research in the field of 3D panoptic perception. Code and dataset are available at https://mias.group/CarlaOcc.

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