LegoOcc++: 一貫性のあるオープンボキャブラリ3D占有予測に向けて
LegoOcc++: Towards Consistent Open-Vocabulary 3D Occupancy Prediction
言語埋め込みガウシアンを用い、二値占有ラベルのみの幾何 supervision で屋内・屋外のオープンボキャブラリ3D占有予測を行う手法を提案。インスタンスレベルの意味一貫性と共視性を考慮した視点選択で断片化を抑制する。
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著者: Changqing Zhou, Yueru Luo, Han Zhang, Zeyu Jiang, Changhao Chen
分類: cs.CV
原文アブストラクト
Open-vocabulary 3D occupancy prediction is important for embodied agents operating in complex environments whose semantic categories extend beyond fixed taxonomies. While recent studies have explored open-vocabulary occupancy in outdoor driving scenarios, indoor environments remain challenging due to dense occlusions, complex geometry, and fine-grained long-tailed semantics. To reduce the need for expensive voxel-level semantic annotations, we study a geometry-only supervision setting requiring only binary occupancy labels. We build upon Language-Embedded Gaussians as a unified representation coupling 3D geometry with language-aligned semantics. For geometry learning, we introduce an opacity-aware Poisson-based Gaussian-to-Occupancy formulation that enables stable volumetric aggregation under binary occupancy supervision. For semantic learning, we render Gaussian features into images and align them with a frozen open-vocabulary model, together with Progressive Temperature Decay to reduce feature mixing during Gaussian splatting. Building on this framework, we further introduce Instance-Level Semantic Consistency to alleviate fragmented object predictions, and Covisibility-Aware Support-View Selection to provide informative and less redundant multi-view semantic supervision. We also extend our framework to outdoor driving scenarios to evaluate its generality across substantially different environments. Extensive experiments on indoor and outdoor benchmarks demonstrate that our method achieves strong open-vocabulary occupancy performance, validating the effectiveness and generality of our framework. Code will be available at https://github.com/JuIvyy/LegoOcc.