# OPUS: Occupancy Prediction Using a Sparse Set

arXiv: https://arxiv.org/abs/2409.09350 · HTML: https://mlsanity.com/paper/2409.09350/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 49 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (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)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [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)
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://mlsanity.com/paper/2206.01256/) (2022)
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://mlsanity.com/paper/2304.14365/) (2023)
- [Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection](https://mlsanity.com/paper/2303.11926/) (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)
- [OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception](https://mlsanity.com/paper/2303.03991/) (2023)
- [FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation](https://mlsanity.com/paper/2307.01492/) (2023)
- [Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion](https://mlsanity.com/paper/2211.10581/) (2022)
- [Rethinking Transformer-based Set Prediction for Object Detection](https://mlsanity.com/paper/2011.10881/) (2020)
- [SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos](https://mlsanity.com/paper/2308.09244/) (2023)
- [RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision](https://mlsanity.com/paper/2309.09502/) (2023)
- [Visual Point Cloud Forecasting enables Scalable Autonomous Driving](https://mlsanity.com/paper/2312.17655/) (2023)
- [COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction](https://mlsanity.com/paper/2312.01919/) (2023)

## Built on by (most cited first, 30 of 56)

- [GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction](https://mlsanity.com/paper/2412.04384/) (2024)
- [GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow](https://mlsanity.com/paper/2502.17288/) (2025)
- [STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction](https://mlsanity.com/paper/2504.19749/) (2025)
- [DVGT: Driving Visual Geometry Transformer](https://mlsanity.com/paper/2512.16919/) (2025)
- [ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions](https://mlsanity.com/paper/2411.07725/) (2024)
- [QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2506.10977/) (2025)
- [DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2409.19972/) (2024)
- [VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving](https://mlsanity.com/paper/2411.14716/) (2024)
- [DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning](https://mlsanity.com/paper/2512.12799/) (2025)
- [SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries](https://mlsanity.com/paper/2510.17482/) (2025)
- [Generalizing Visual Geometry Priors to Sparse Gaussian Occupancy Prediction](https://mlsanity.com/paper/2602.21552/) (2026)
- [Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2504.12959/) (2025)
- [SplatSSC: Decoupled Depth-Guided Gaussian Splatting for Semantic Scene Completion](https://mlsanity.com/paper/2508.02261/) (2025)
- [LightOcc: Lightweight Spatial Embedding for Efficient Vision-based 3D Occupancy Prediction](https://mlsanity.com/paper/2412.05976/) (2024)
- [GSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting](https://mlsanity.com/paper/2412.14579/) (2024)
- [ShelfOcc: Native 3D Supervision beyond LiDAR for Vision-Based Occupancy Estimation](https://mlsanity.com/paper/2511.15396/) (2025)
- [SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Driving](https://mlsanity.com/paper/2509.16588/) (2025)
- [SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model](https://mlsanity.com/paper/2511.22039/) (2025)
- [VG3T: Visual Geometry Grounded Gaussian Transformer](https://mlsanity.com/paper/2512.05988/) (2025)
- [SparseOccVLA: Bridging Occupancy and Vision-Language Models via Sparse Queries for Unified 4D Scene Understanding and Planning](https://mlsanity.com/paper/2601.06474/) (2026)
- [Enhancing Indoor Occupancy Prediction via Sparse Query-Based Multi-Level Consistent Knowledge Distillation](https://mlsanity.com/paper/2602.02318/) (2026)
- [Event-aided Semantic Scene Completion](https://mlsanity.com/paper/2502.02334/) (2025)
- [See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction](https://mlsanity.com/paper/2505.20641/) (2025)
- [ODG: Occupancy Prediction Using Dual Gaussians](https://mlsanity.com/paper/2506.09417/) (2025)
- [GaussianFusionOcc: A Seamless Sensor Fusion Approach for 3D Occupancy Prediction Using 3D Gaussians](https://mlsanity.com/paper/2507.18522/) (2025)
- [UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs](https://mlsanity.com/paper/2511.01768/) (2025)
- [Latent Gaussian Splatting for 4D Panoptic Occupancy Tracking](https://mlsanity.com/paper/2602.23172/) (2026)
- [Out-of-Distribution Semantic Occupancy Prediction](https://mlsanity.com/paper/2506.21185/) (2025)
- [Geometry-Grounded Unified 3D Perception for Autonomous Driving](https://mlsanity.com/paper/2608.13147/) (2026)
- [TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness](https://mlsanity.com/paper/2503.09941/) (2025)

## Abstract (arXiv)

Occupancy prediction, aiming at predicting the occupancy status within voxelized 3D environment, is quickly gaining momentum within the autonomous driving community. Mainstream occupancy prediction works first discretize the 3D environment into voxels, then perform classification on such dense grids. However, inspection on sample data reveals that the vast majority of voxels is unoccupied. Performing classification on these empty voxels demands suboptimal computation resource allocation, and reducing such empty voxels necessitates complex algorithm designs. To this end, we present a novel perspective on the occupancy prediction task: formulating it as a streamlined set prediction paradigm without the need for explicit space modeling or complex sparsification procedures. Our proposed framework, called OPUS, utilizes a transformer encoder-decoder architecture to simultaneously predict occupied locations and classes using a set of learnable queries. Firstly, we employ the Chamfer distance loss to scale the set-to-set comparison problem to unprecedented magnitudes, making training such model end-to-end a reality. Subsequently, semantic classes are adaptively assigned using nearest neighbor search based on the learned locations. In addition, OPUS incorporates a suite of non-trivial strategies to enhance model performance, including coarse-to-fine learning, consistent point sampling, and adaptive re-weighting, etc. Finally, compared with current state-of-the-art methods, our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.