UnsOcc: レンダリング融合による非構造シーンでの3Dセマンティック占有予測
UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusion
非構造シーン（例：露天鉱山）向けに、レンダリングベースの融合モジュールとガウススプラッティングによる詳細補助監視を導入したマルチモーダル3Dセマンティック占有予測フレームワークを提案し、長尾カテゴリの性能を向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Ye Wu, Ruiqi Song, Baiyong Ding, Nanxin Zeng, Junjie Cheng, Yunfeng Ai
分類: cs.CV, cs.RO
原文アブストラクト
Unstructured scenes present unique challenges for autonomous driving, as irregular obstacles and sparse scene layouts undermine the effectiveness of traditional perception methods such as 3D object detection. 3D semantic occupancy prediction has emerged as a prominent focus due to its ability to provide dense spatial representations by assigning semantic labels to individual voxels in 3D space. However, directly applying 3D semantic occupancy prediction to unstructured scenes remains challenging because scene sparsity hinders effective cross-modal fusion and the more severe long-tail distribution in these scenarios further degrades prediction performance. To validate the effectiveness of our approach, we construct a dedicated dataset of unstructured scenes collected from open-pit mines. Based on this, we propose UnsOcc, a multi-modal 3D semantic occupancy prediction framework that improves robustness in unstructured environments. At its core, we introduce a rendering-based fusion module, RenderFusion, which enhances cross-modal feature alignment through bidirectional rendering supervision. Furthermore, we propose GSRefinement, a detail-aware auxiliary supervision method based on Gaussian Splatting that projects sparse 3D occupancy predictions into dense 2D semantic segmentation maps, enabling effective supervision for long-tail categories. Extensive experiments on both the open-pit mine dataset and the nuScenes dataset demonstrate that our method significantly outperforms existing state-of-the-art approaches.
