3D semantic occupancy prediction is a pivotal task in autonomous driving, providing a dense and fine-grained understanding of the surrounding environment, yet single-modality methods face trade-offs between camera semantics and LiDAR geometry. Existing multi-modal frameworks often struggle with modality heterogeneity, spatial misalignment, and the representation crisis--where voxels are computationally heavy and BEV alternatives are lossy. We present GaussianOcc3D, a multi-modal framework bridging camera and LiDAR through a memory-efficient, continuous 3D Gaussian representation. We introduce four modules: (1) LiDAR Depth Feature Aggregation (LDFA), using depth-wise deformable sampling to lift sparse signals onto Gaussian primitives; (2) Entropy-Based Feature Smoothing (EBFS) to mitigate domain noise; (3) Adaptive Camera-LiDAR Fusion (ACLF) with uncertainty-aware reweighting for sensor reliability; and (4) a Gauss-Mamba Head leveraging Selective State Space Models for global context with linear complexity. Evaluations on Occ3D, SurroundOcc, and SemanticKITTI benchmarks demonstrate state-of-the-art performance, achieving mIoU scores of 49.4%, 28.9%, and 25.2% respectively. GaussianOcc3D exhibits superior robustness across challenging rainy and nighttime conditions.
Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；HKUST(GZ)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室，北京航空航天大学，北京，中国）；School of Computer Science and Engineering, Beihang University, Beijing, China（计算机科学与工程学院，北京航空航天大学，北京，中国）；School of Artificial Intelligence, Beihang University, Beijing, China（人工智能学院，北京航空航天大学，北京，中国）
AdaOcc: Adaptive 3D Occupancy Prediction for Embodied Tasks
Beihang University（北京航空航天大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；XYZ Embodied AI（XYZ具身智能公司）；Hebei University of Technology（河北工业大学）；ShanghaiTech University（上海科技大学）；University of Science and Technology Beijing（北京科技大学）
TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness
China Telecom Research Institute（中国电信研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）
AGO: Adaptive Grounding for Open World 3D Occupancy Prediction
Mercedes-Benz AG（梅赛德斯-奔驰集团）；University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；University of Bonn（波恩大学）；RPL；KTH Royal Institute of Technology（皇家理工学院）；TU Berlin（柏林技术大学）
Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception
School of Automotive Studies, Tongji University（同济大学汽车学院）；Momoni AI；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Chair of Automotive Engineering, Technische Universität Berlin（柏林技术大学汽车工程学系）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市学院信息与电气工程学院）
UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusion
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Waytous Inc.（Waytous公司）
WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction
Research Center for Intelligent Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能计算系统研究中心）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
DSOcc: Leveraging Depth Awareness and Semantic Aid to Boost Camera-Based 3D Semantic Occupancy Prediction
S-Lab & College of Computing and Data Science, Nanyang Technological University（S实验室及计算与数据科学学院，南洋理工大学）；MMLab, The Chinese University of Hong Kong（M实验室，香港中文大学）；State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University（流体动力与机电系统国家重点实验室，浙江大学）；SenseTime Research, Hong Kong（商汤研究，香港）
EmbodiedOcc++: Boosting Embodied 3D Occupancy Prediction with Plane Regularization and Uncertainty Sampler
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；School of electronic science and engineering, Nanjing University（电子科学与工程学院，南京大学）；Berkeley Artificial Intelligence Research Lab, Department of EECS, University of California, Berkeley（伯克利人工智能研究实验室，电子工程与计算机科学系，加州大学伯克利分校）
InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanyang Technological University（南洋理工大学）；Huawei Technologies Ltd.（华为技术有限公司）；University of New South Wales（新南威尔士大学）
LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Camera
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学 cyber 系统与控制研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Technical University of Munich（慕尼黑工业大学）
Hikvision Research Institute, Hangzhou Hikvision Digital Technology Co. Ltd（杭州海康威视数字技术股份有限公司海康研究院）；Shanghai Jiao Tong University（上海交通大学）
3D vehicle detection based on multi-modal fusion is an important task of many applications such as autonomous driving. Although significant progress has been made, we still observe two aspects that need to be further improvement: First, the specific gain that camera images can bring to 3D detection is seldom explored by previous works. Second, many fusion algorithms run slowly, which is essential for applications with high real-time requirements(autonomous driving). To this end, we propose an end-to-end trainable single-stage multi-modal feature adaptive network in this paper, which uses image…
MetaOcc: Spatio-Temporal Fusion of Surround-View 4D Radar and Camera for 3D Occupancy Prediction with Dual Training Strategies
School of Automotive Studies, Tongji University（同济大学汽车学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Automobile, Chang'an University（长安大学汽车学院）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市学院信息与电气工程学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）
GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院，交通数据挖掘与具身智能北京市重点实验室）；School of Artificial Intelligence (School of Software), Yanshan University（燕山大学人工智能学院（软件学院））；University of Macau（澳门大学）；Nanyang Technological University（南洋理工大学）；The University of Queensland（昆士兰大学）
Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road ac…
Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Alibaba Group（阿里巴巴集团）；Singapore Management University（新加坡管理学院）
MS-Occ: Multi-Stage LiDAR-Camera Fusion for 3D Semantic Occupancy Prediction
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology（光学电子与计算机工程学院，上海科学技术大学）；School of Automotive Studies, Tongji University（汽车学院，同济大学）；Momoni AI；College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；School of Engineering, Swinburne University of Technology（工程学院，斯威本技术大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电气与电子工程学院，南洋理工大学）
HiMAP: History-aware Map-occupancy Prediction with Fallback
Institute of Cartography and Geoinformatics, Leibniz University Hannover（莱比锡大学汉诺威分校制图与地理信息学研究所）；Institute of Information Processing, Leibniz University Hannover（莱比锡大学汉诺威分校信息处理研究所）；Faculty of Geo-Information Science and Earth Observation, University of Twente（埃因霍温大学地球信息科学与地球观测系）
Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；College of Computer Science, Nankai University（南开大学计算机学院）；Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）
