O3N is a novel framework that performs open‑vocabulary occupancy prediction from a single omnidirectional RGB image. It introduces a polar‑spiral voxel embedding (PsM) for continuous 360° spatial representation, an Occupancy Cost Aggregation (OCA) module that unifies geometric and semantic supervision, and a Natural Modality Alignment (NMA) pathway that aligns visual, voxel, and text features. Experiments show state‑of‑the‑art results on QuadOcc and Human360Occ benchmarks, with strong cross‑scene generalization and semantic scalability.
By Mengfei Duan, Hao Shi, Fei Teng, Guoqiang Zhao, Yuheng Zhang, Zhiyong Li, Kailun Yang
The paper introduces Out-of-Distribution Semantic Occupancy Prediction, a task that focuses on detecting unknown objects in 3D voxel space for autonomous driving. It proposes Realistic Anomaly Augmentation to create two new datasets, VAA-KITTI and VAA-KITTI-360, and presents the OccOoD framework, which uses Cross‑Space Semantic Refinement to improve OoD detection while maintaining semantic occupancy accuracy. Experiments show OccOoD achieves an AuROC of 65.50% and an AuPRCr of 31.83% within a 1.2 m radius, demonstrating strong generalization to real‑world urban scenes.
By Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Ruiping Liu, Fei Teng, Kai Luo, Zhiyong Li, Kailun Yang
LetOccVote is a weakly supervised framework for 3D occupancy prediction that uses 2D pseudo-labels from vision foundation models. It introduces cross‑frame voting mechanisms—Depth Vote for geometric consistency and Semantic Vote for semantic reliability—to filter out erroneous pseudo-labels before training. The method achieves state‑of‑the‑art results on Occ3D‑nuScenes using only 2D supervision.
By Chi Zhang, Qi Song, Feifei Li, Jie Li, Rui Huang
arXiv:2610.07569v1 Announce Type: cross
Abstract: Dense 3D mapping with semantic understanding is essential for robotic perception in complex environments. Recent 3D Gaussian Splatting-based mapping...
By Binh Long Nguyen, Kien Nguyen, Clinton Fookes, Peyman Moghadam
OccAnyScene introduces a unified approach for 3D occupancy prediction across both indoor and outdoor scenes, addressing the challenge of handling diverse camera setups, spatial ranges, voxel specifications, and semantic taxonomies. The method builds on a pretrained depth model, using pixel-aligned frustum feature aggregation and frustum-parameterized Gaussian construction to generate scene-adaptive occupancy predictions. It achieves state-of-the-art performance, scoring 59.92% mIoU on Occ-ScanNet and 23.06% mIoU on SurroundOcc-nuScenes.
By Junjie Liu, Wanshui Gan, Zitong Dai, Guiping Cao, Yan Li, Ke Chen, Dongmei Jiang, Jianguo Zhang, Xiangyuan Lan
arXiv:2511.16949v2 Announce Type: replace-cross
Abstract: Dense 3D semantic occupancy perception is critical for mobile robots operating in pedestrian-rich environments, yet it remains underexplored...
By Junseo Kim, Guido Dumont, Xinyu Gao, Gang Chen, Holger Caesar, Javier Alonso-Mora
CoCam4D is a Bayesian framework for camera-only autonomous driving that models geometric uncertainty in collaborative perception. It employs a VGGT-based network to produce 3D Gaussian scene representations with uncertainty estimates, allowing multiple vehicles to fuse observations without LiDAR. The system introduces Dynamic Object Primitives, a 35‑byte format for efficient C‑V2X communication, and shows significant performance gains over recent vision-only methods on OPV2V+ and DAIR‑V2X‑C benchmarks.
By Soham Pahari, Sudip Das, Arindam Das, Ujjwal Bhattacharya
CrowdOcc introduces a new RGB‑D dataset and monocular semantic scene completion framework tailored for quadruped robots navigating crowded indoor spaces. The dataset comprises 25.1K frames across 11 scenes, with annotations derived via static‑dynamic decoupling. The framework employs Normal Guided Scene Geometry Fusion and Human‑Centric Sparse Interaction to robustly model occluded geometry and human‑scene relationships, achieving state‑of‑the‑art performance on the dataset’s test set.
By Feiyang Chen, Jincheng Hu, Yiduo Chen, Jihao Li, Yue Liang, Bingzhao Gao, Yanjun Huang, Yuanjian Zhang
arXiv:2605.25059v4 Announce Type: replace
Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
By Ruoyu Wang, Yong Liu, Jiahan Li, Sheng Tao, Yuhang Lin, Yukai Ma
arXiv:2607. 19036v1 Announce Type: cross Abstract: V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative agents.
By Zhihao Yang, Zhiyu Xiang, Peng Xu, Tianyu Pu, Kai Wang, Eryun Liu, Dongping Zhang, Yong Ding
Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as it can model holistic 3D spaces by encoding geome...
arXiv:2507.22052v3 Announce Type: replace
Abstract: We present Ov3R, a novel framework for open-vocabulary semantic 3D reconstruction from RGB video streams, designed to advance Spatial AI. The syste...
By Ziren Gong, Xiaohan Li, Fabio Tosi, Jiawei Han, Stefano Mattoccia, Jianfei Cai, Matteo Poggi