# Vision-based 3D occupancy prediction in autonomous driving: a review and outlook

arXiv: https://arxiv.org/abs/2405.02595 · HTML: https://mlsanity.com/paper/2405.02595/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 100 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 30 of 75)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2302.07817/) (2023)
- [MonoScene: Monocular 3D Semantic Scene Completion](https://mlsanity.com/paper/2112.00726/) (2021)
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://mlsanity.com/paper/2304.14365/) (2023)
- [SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving](https://mlsanity.com/paper/2303.09551/) (2023)
- [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://mlsanity.com/paper/2302.12251/) (2023)
- [OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2304.05316/) (2023)
- [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving](https://mlsanity.com/paper/2311.16038/) (2023)
- [Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion](https://mlsanity.com/paper/2012.03762/) (2020)
- [OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception](https://mlsanity.com/paper/2303.03991/) (2023)
- [Scene as Occupancy](https://mlsanity.com/paper/2306.02851/) (2023)

## Built on by (most cited first, 29 of 29)

- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective](https://mlsanity.com/paper/2405.05173/) (2024)
- [OccMamba: Semantic Occupancy Prediction with State Space Models](https://mlsanity.com/paper/2408.09859/) (2024)
- [UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.24381/) (2025)
- [GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow](https://mlsanity.com/paper/2502.17288/) (2025)
- [An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training](https://mlsanity.com/paper/2412.13772/) (2024)
- [LangOcc: Self-Supervised Open Vocabulary Occupancy Estimation via Volume Rendering](https://mlsanity.com/paper/2407.17310/) (2024)
- [GaussianFormer3D: Multi-Modal Gaussian-based Semantic Occupancy Prediction with 3D Deformable Attention](https://mlsanity.com/paper/2505.10685/) (2025)
- [Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2504.12959/) (2025)
- [ViPOcc: Leveraging Visual Priors from Vision Foundation Models for Single-View 3D Occupancy Prediction](https://mlsanity.com/paper/2412.11210/) (2024)
- [Generative AI for Autonomous Driving: Frontiers and Opportunities](https://mlsanity.com/paper/2505.08854/) (2025)
- [LightOcc: Lightweight Spatial Embedding for Efficient Vision-based 3D Occupancy Prediction](https://mlsanity.com/paper/2412.05976/) (2024)
- [OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction](https://mlsanity.com/paper/2503.10605/) (2025)
- [OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective](https://mlsanity.com/paper/2512.20770/) (2025)
- [Progressive Gaussian Transformer with Anisotropy-aware Sampling for Open Vocabulary Occupancy Prediction](https://mlsanity.com/paper/2510.04759/) (2025)
- [Event-aided Semantic Scene Completion](https://mlsanity.com/paper/2502.02334/) (2025)
- [SA-Occ: Satellite-Assisted 3D Occupancy Prediction in Real World](https://mlsanity.com/paper/2503.16399/) (2025)
- [IPFormer: Visual 3D Panoptic Scene Completion with Context-Adaptive Instance Proposals](https://mlsanity.com/paper/2506.20671/) (2025)
- [Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes](https://mlsanity.com/paper/2505.09562/) (2025)
- [S2GO: Streaming Sparse Gaussian Occupancy Prediction](https://mlsanity.com/paper/2506.05473/) (2025)
- [A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X-Enabled Autonomous Driving](https://mlsanity.com/paper/2506.17004/) (2025)
- [VGGT-Occ: Geometry-Grounded and Density-Aware Gated Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2605.16911/) (2026)
- [DGOcc: Depth-aware Global Query-based Network for Monocular 3D Occupancy Prediction](https://mlsanity.com/paper/2504.07524/) (2025)
- [TACOcc:Target-Adaptive Cross-Modal Fusion with Volume Rendering for 3D Semantic Occupancy](https://mlsanity.com/paper/2505.12693/) (2025)
- [Rebenchmarking Unsupervised Monocular 3D Occupancy Prediction](https://mlsanity.com/paper/2602.06488/) (2026)
- [BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion](https://mlsanity.com/paper/2603.09961/) (2026)
- [TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios](https://mlsanity.com/paper/2603.29759/) (2026)
- [Group-wise Supervision with Focal-Dice Loss for Long-Tailed Indoor Semantic Occupancy Prediction](https://mlsanity.com/paper/2607.28935/) (2026)
- [LetOccVote: Learning Weakly Supervised 3D Occupancy through Consensus](https://mlsanity.com/paper/2609.04846/) (2026)

## Abstract (arXiv)

In recent years, autonomous driving has garnered escalating attention for its potential to relieve drivers' burdens and improve driving safety. Vision-based 3D occupancy prediction, which predicts the spatial occupancy status and semantics of 3D voxel grids around the autonomous vehicle from image inputs, is an emerging perception task suitable for cost-effective perception system of autonomous driving. Although numerous studies have demonstrated the greater advantages of 3D occupancy prediction over object-centric perception tasks, there is still a lack of a dedicated review focusing on this rapidly developing field. In this paper, we first introduce the background of vision-based 3D occupancy prediction and discuss the challenges in this task. Secondly, we conduct a comprehensive survey of the progress in vision-based 3D occupancy prediction from three aspects: feature enhancement, deployment friendliness and label efficiency, and provide an in-depth analysis of the potentials and challenges of each category of methods. Finally, we present a summary of prevailing research trends and propose some inspiring future outlooks. To provide a valuable reference for researchers, a regularly updated collection of related papers, datasets, and codes is organized at https://github.com/zya3d/Awesome-3D-Occupancy-Prediction.