# EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving

arXiv: https://arxiv.org/abs/2406.07042 · HTML: https://mlsanity.com/paper/2406.07042/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 30 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [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)
- [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)
- [PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation](https://mlsanity.com/paper/2306.10013/) (2023)
- [FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin](https://mlsanity.com/paper/2311.12058/) (2023)
- [SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction](https://mlsanity.com/paper/2404.09502/) (2024)
- [Fully Sparse 3D Occupancy Prediction](https://mlsanity.com/paper/2312.17118/) (2023)
- [FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View](https://mlsanity.com/paper/2403.02710/) (2024)
- [Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2404.04561/) (2024)
- [UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View](https://mlsanity.com/paper/2303.15083/) (2023)
- [DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation](https://mlsanity.com/paper/2309.15109/) (2023)
- [OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving](https://mlsanity.com/paper/2404.15014/) (2024)
- [Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection](https://mlsanity.com/paper/2311.07152/) (2023)
- [RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation](https://mlsanity.com/paper/2312.11829/) (2023)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2403.05329/) (2024)

## Built on by (most cited first, 8 of 8)

- [OPUS: Occupancy Prediction Using a Sparse Set](https://mlsanity.com/paper/2409.09350/) (2024)
- [ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions](https://mlsanity.com/paper/2411.07725/) (2024)
- [An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training](https://mlsanity.com/paper/2412.13772/) (2024)
- [DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2409.19972/) (2024)
- [Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots](https://mlsanity.com/paper/2603.13108/) (2026)
- [OccStress: Stress-Testing the 4D Occupancy Forecasting Chain](https://mlsanity.com/paper/2512.15621/) (2025)
- [TFusionOcc: T-Primitive Based Object-Centric Multi-Sensor Fusion Framework for 3D Occupancy Prediction](https://mlsanity.com/paper/2602.06400/) (2026)
- [Height-Guided Projection Reparameterization for Camera-LiDAR Occupancy](https://mlsanity.com/paper/2605.05072/) (2026)

## Abstract (arXiv)

3D occupancy prediction (3DOcc) is a rapidly rising and challenging perception task in the field of autonomous driving. Existing 3D occupancy networks (OccNets) are both computationally heavy and label-hungry. In terms of model complexity, OccNets are commonly composed of heavy Conv3D modules or transformers at the voxel level. Moreover, OccNets are supervised with expensive large-scale dense voxel labels. Model and data inefficiencies, caused by excessive network parameters and label annotation requirements, severely hinder the onboard deployment of OccNets. This paper proposes an EFFicient Occupancy learning framework, EFFOcc, that targets minimal network complexity and label requirements while achieving state-of-the-art accuracy. We first propose an efficient fusion-based OccNet that only uses simple 2D operators and improves accuracy to the state-of-the-art on three large-scale benchmarks: Occ3D-nuScenes, Occ3D-Waymo, and OpenOccupancy-nuScenes. On the Occ3D-nuScenes benchmark, the fusion-based model with ResNet-18 as the image backbone has 21.35M parameters and achieves 51.49 in terms of mean Intersection over Union (mIoU). Furthermore, we propose a multi-stage occupancy-oriented distillation to efficiently transfer knowledge to vision-only OccNet. Extensive experiments on occupancy benchmarks show state-of-the-art precision for both fusion-based and vision-based OccNets. For the demonstration of learning with limited labels, we achieve 94.38% of the performance (mIoU = 28.38) of a 100% labeled vision OccNet (mIoU = 30.07) using the same OccNet trained with only 40% labeled sequences and distillation from the fusion-based OccNet.