# Semantic 3D Occupancy Mapping through Efficient High Order CRFs

arXiv: https://arxiv.org/abs/1707.07388 · HTML: https://mlsanity.com/paper/1707.07388/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2017.

## Built on by (most cited first, 15 of 15)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments](https://mlsanity.com/paper/2403.09412/) (2024)
- [ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions](https://mlsanity.com/paper/2411.07725/) (2024)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting](https://mlsanity.com/paper/2411.02547/) (2024)
- [ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty](https://mlsanity.com/paper/2310.16020/) (2023)
- [Convolutional Bayesian Kernel Inference for 3D Semantic Mapping](https://mlsanity.com/paper/2209.10663/) (2022)
- [Semantic Causality-Aware Vision-Based 3D Occupancy Prediction](https://mlsanity.com/paper/2509.08388/) (2025)
- [PanopticNDT: Efficient and Robust Panoptic Mapping](https://mlsanity.com/paper/2309.13635/) (2023)
- [Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference](https://mlsanity.com/paper/2403.14138/) (2024)
- [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference](https://mlsanity.com/paper/2108.03180/) (2021)
- [Interactive Semantic Map Representation for Skill-based Visual Object Navigation](https://mlsanity.com/paper/2311.04107/) (2023)
- [Advancing Frontiers in SLAM: A Survey of Symbolic Representation and Human-Machine Teaming in Environmental Mapping](https://mlsanity.com/paper/2405.01398/) (2024)

## Abstract (arXiv)

Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.