OccAny: Generalized Unconstrained Urban 3D Occupancy
Anh-Quan Cao, Tuan-Hung Vu

TL;DR
OccAny is a novel urban 3D occupancy prediction model that generalizes to out-of-domain scenes, combining segmentation and novel view rendering to improve geometry completion and outperform existing methods.
Contribution
It introduces the first generalized 3D occupancy framework with segmentation forcing and a novel view rendering pipeline for urban scenes.
Findings
Outperforms all visual geometry baselines on 3D occupancy prediction
Remains competitive with in-domain self-supervised methods
Effective across multiple input settings and datasets
Abstract
Relying on in-domain annotations and precise sensor-rig priors, existing 3D occupancy prediction methods are limited in both scalability and out-of-domain generalization. While recent visual geometry foundation models exhibit strong generalization capabilities, they were mainly designed for general purposes and lack one or more key ingredients required for urban occupancy prediction, namely metric prediction, geometry completion in cluttered scenes and adaptation to urban scenarios. We address this gap and present OccAny, the first unconstrained urban 3D occupancy model capable of operating on out-of-domain uncalibrated scenes to predict and complete metric occupancy coupled with segmentation features. OccAny is versatile and can predict occupancy from sequential, monocular, or surround-view images. Our contributions are three-fold: (i) we propose the first generalized 3D occupancy…
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Taxonomy
TopicsAdvanced Vision and Imaging · Robotics and Sensor-Based Localization · Video Surveillance and Tracking Methods
