# Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments

arXiv: https://arxiv.org/abs/2011.03981 · HTML: https://mlsanity.com/paper/2011.03981/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2020.

## Built on by (most cited first, 21 of 21)

- [Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2404.04561/) (2024)
- [MonoOcc: Digging into Monocular Semantic Occupancy Prediction](https://mlsanity.com/paper/2403.08766/) (2024)
- [DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2409.19972/) (2024)
- [AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting](https://mlsanity.com/paper/2502.04981/) (2025)
- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [SceneSense: Diffusion Models for 3D Occupancy Synthesis from Partial Observation](https://mlsanity.com/paper/2403.11985/) (2024)
- [FSF-Net: Enhance 4D Occupancy Forecasting with Coarse BEV Scene Flow for Autonomous Driving](https://mlsanity.com/paper/2409.15841/) (2024)
- [Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation](https://mlsanity.com/paper/2409.10681/) (2024)
- [Enhancing Indoor Occupancy Prediction via Sparse Query-Based Multi-Level Consistent Knowledge Distillation](https://mlsanity.com/paper/2602.02318/) (2026)
- [4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration](https://mlsanity.com/paper/2402.17904/) (2024)
- [Global-Aware Monocular Semantic Scene Completion with State Space Models](https://mlsanity.com/paper/2503.06569/) (2025)
- [Test-Time 3D Occupancy Prediction](https://mlsanity.com/paper/2503.08485/) (2025)
- [What we see and What we don't see: Imputing Occluded Crowd Structures from Robot Sensing](https://mlsanity.com/paper/2109.08494/) (2021)
- [FloorGenT: Generative Vector Graphic Model of Floor Plans for Robotics](https://mlsanity.com/paper/2203.03385/) (2022)
- [SePaint: Semantic Map Inpainting via Multinomial Diffusion](https://mlsanity.com/paper/2303.02737/) (2023)
- [VPOcc: Exploiting Vanishing Point for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2408.03551/) (2024)
- [Complementary Information Guided Occupancy Prediction via Multi-Level Representation Fusion](https://mlsanity.com/paper/2510.13198/) (2025)
- [GoToNet: Fast Monocular Scene Exposure and Exploration](https://mlsanity.com/paper/2206.05967/) (2022)
- [Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis](https://mlsanity.com/paper/2506.20049/) (2025)
- [Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Robotics](https://mlsanity.com/paper/2507.09340/) (2025)

## Abstract (arXiv)

In autonomous navigation of mobile robots, sensors suffer from massive occlusion in cluttered environments, leaving significant amount of space unknown during planning. In practice, treating the unknown space in optimistic or pessimistic ways both set limitations on planning performance, thus aggressiveness and safety cannot be satisfied at the same time. However, humans can infer the exact shape of the obstacles from only partial observation and generate non-conservative trajectories that avoid possible collisions in occluded space. Mimicking human behavior, in this paper, we propose a method based on deep neural network to predict occupancy distribution of unknown space reliably. Specifically, the proposed method utilizes contextual information of environments and learns from prior knowledge to predict obstacle distributions in occluded space. We use unlabeled and no-ground-truth data to train our network and successfully apply it to real-time navigation in unseen environments without any refinement. Results show that our method leverages the performance of a kinodynamic planner by improving security with no reduction of speed in clustered environments.