FreeOcc: 学習不要の身体化オープンボキャブラリ占有予測
FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction
3Dアノテーションやカメラ姿勢の真値を使わず、SLAMと視覚言語モデルを組み合わせて、単眼またはRGB-Dシーケンスからオープンボキャブラリの占有マップを構築する学習不要のフレームワークを提案した。
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著者: Zeyu Jiang, Changqing Zhou, Xingxing Zuo, Changhao Chen
分類: cs.RO, cs.CV
原文アブストラクト
Existing learning-based occupancy prediction methods rely on large-scale 3D annotations and generalize poorly across environments. We present FreeOcc, a training-free framework for open-vocabulary occupancy prediction from monocular or RGB-D sequences. Unlike prior approaches that require voxel-level supervision and ground-truth camera poses, FreeOcc operates without 3D annotations, pose ground truth, or any learning stage. FreeOcc incrementally builds a globally consistent occupancy map via a four-layer pipeline: a SLAM backbone estimates poses and sparse geometry; a geometrically consistent Gaussian update constructs dense 3D Gaussian maps; open-vocabulary semantics from off-the-shelf vision-language models are associated with Gaussian primitives; and a probabilistic Gaussian-to-occupancy projection produces dense voxel occupancy. Despite being entirely training-free and pose-agnostic, FreeOcc achieves over $2\times$ improvements in IoU and mIoU on EmbodiedOcc-ScanNet compared to prior self-supervised methods. We further introduce ReplicaOcc, a benchmark for indoor open-vocabulary occupancy prediction, and show that FreeOcc transfers zero-shot to novel environments, substantially outperforming both supervised and self-supervised baselines. Project page: https://the-masses.github.io/freeocc-web/.
