# UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering

arXiv: https://arxiv.org/abs/2306.09117 · HTML: https://mlsanity.com/paper/2306.09117/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2302.07817/) (2023)
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://mlsanity.com/paper/2304.14365/) (2023)
- [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://mlsanity.com/paper/2302.12251/) (2023)
- [OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception](https://mlsanity.com/paper/2303.03991/) (2023)
- [TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning](https://mlsanity.com/paper/2212.13979/) (2022)

## Built on by (most cited first, 26 of 26)

- [FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin](https://mlsanity.com/paper/2311.12058/) (2023)
- [Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications](https://mlsanity.com/paper/2311.17663/) (2023)
- [Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2404.04561/) (2024)
- [MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations](https://mlsanity.com/paper/2311.11762/) (2023)
- [Vision-based 3D occupancy prediction in autonomous driving: a review and outlook](https://mlsanity.com/paper/2405.02595/) (2024)
- [GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision](https://mlsanity.com/paper/2405.10591/) (2024)
- [ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions](https://mlsanity.com/paper/2411.07725/) (2024)
- [Neural Radiance Field in Autonomous Driving: A Survey](https://mlsanity.com/paper/2404.13816/) (2024)
- [AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting](https://mlsanity.com/paper/2502.04981/) (2025)
- [Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving](https://mlsanity.com/paper/2312.11837/) (2023)
- [SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field](https://mlsanity.com/paper/2403.14366/) (2024)
- [PanoSSC: Exploring Monocular Panoptic 3D Scene Reconstruction for Autonomous Driving](https://mlsanity.com/paper/2406.07037/) (2024)
- [Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2504.12959/) (2025)
- [Dense Point Clouds Matter: Dust-GS for Scene Reconstruction from Sparse Viewpoints](https://mlsanity.com/paper/2409.08613/) (2024)
- [Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting](https://mlsanity.com/paper/2411.14169/) (2024)
- [HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving](https://mlsanity.com/paper/2511.07106/) (2025)
- [SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations](https://mlsanity.com/paper/2309.10527/) (2023)
- [Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes](https://mlsanity.com/paper/2505.09562/) (2025)
- [OcRFDet: Object-Centric Radiance Fields for Multi-View 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2506.23565/) (2025)
- [CurriFlow: Curriculum-Guided Depth Fusion with Optical Flow-Based Temporal Alignment for 3D Semantic Scene Completion](https://mlsanity.com/paper/2510.12362/) (2025)
- [TACOcc:Target-Adaptive Cross-Modal Fusion with Volume Rendering for 3D Semantic Occupancy](https://mlsanity.com/paper/2505.12693/) (2025)
- [FMOcc: TPV-Driven Flow Matching for 3D Occupancy Prediction with Selective State Space Model](https://mlsanity.com/paper/2507.02250/) (2025)
- [SparseOcc++: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction](https://mlsanity.com/paper/2607.04732/) (2026)
- [SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion](https://mlsanity.com/paper/2609.17413/) (2026)
- [Risk-Aware Occupancy for Safety-Oriented End-to-End Autonomous Driving](https://mlsanity.com/paper/2609.21470/) (2026)
- [SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts](https://mlsanity.com/paper/2610.15745/) (2026)

## Abstract (arXiv)

In this technical report, we present our solution, named UniOCC, for the Vision-Centric 3D occupancy prediction track in the nuScenes Open Dataset Challenge at CVPR 2023. Existing methods for occupancy prediction primarily focus on optimizing projected features on 3D volume space using 3D occupancy labels. However, the generation process of these labels is complex and expensive (relying on 3D semantic annotations), and limited by voxel resolution, they cannot provide fine-grained spatial semantics. To address this limitation, we propose a novel Unifying Occupancy (UniOcc) prediction method, explicitly imposing spatial geometry constraint and complementing fine-grained semantic supervision through volume ray rendering. Our method significantly enhances model performance and demonstrates promising potential in reducing human annotation costs. Given the laborious nature of annotating 3D occupancy, we further introduce a Depth-aware Teacher Student (DTS) framework to enhance prediction accuracy using unlabeled data. Our solution achieves 51.27% mIoU on the official leaderboard with single model, placing 3rd in this challenge.