# Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots

arXiv: https://arxiv.org/abs/2008.11383 · HTML: https://mlsanity.com/paper/2008.11383/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 20 of 20)

- [PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching](https://mlsanity.com/paper/2103.07094/) (2021)
- [RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing](https://mlsanity.com/paper/2309.10356/) (2023)
- [Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation](https://mlsanity.com/paper/2104.12861/) (2021)
- [Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2103.02433/) (2021)
- [SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection](https://mlsanity.com/paper/2107.14599/) (2021)
- [Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms](https://mlsanity.com/paper/2204.13590/) (2022)
- [End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving](https://mlsanity.com/paper/2104.08862/) (2021)
- [Fast Road Segmentation via Uncertainty-aware Symmetric Network](https://mlsanity.com/paper/2203.04537/) (2022)
- [D2NT: A High-Performing Depth-to-Normal Translator](https://mlsanity.com/paper/2304.12031/) (2023)
- [RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion](https://mlsanity.com/paper/2407.21631/) (2024)
- [SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection](https://mlsanity.com/paper/2402.18918/) (2024)
- [Pseudo-LiDAR Based Road Detection](https://mlsanity.com/paper/2107.13279/) (2021)
- [SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume](https://mlsanity.com/paper/2107.08187/) (2021)
- [Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection](https://mlsanity.com/paper/2112.13082/) (2021)
- [Adaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy Features](https://mlsanity.com/paper/2304.13979/) (2023)
- [Depth Matters: Exploring Deep Interactions of RGB-D for Semantic Segmentation in Traffic Scenes](https://mlsanity.com/paper/2409.07995/) (2024)
- [Computer-Aided Road Inspection: Systems and Algorithms](https://mlsanity.com/paper/2203.02355/) (2022)
- [Co-Teaching: An Ark to Unsupervised Stereo Matching](https://mlsanity.com/paper/2107.08186/) (2021)
- [Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline](https://mlsanity.com/paper/2210.02991/) (2022)
- [Resilient Multimodal Industrial Surface Defect Detection with Uncertain Sensors Availability](https://mlsanity.com/paper/2509.02962/) (2025)

## Abstract (arXiv)

The joint detection of drivable areas and road anomalies is a crucial task for ground mobile robots. In recent years, many impressive semantic segmentation networks, which can be used for pixel-level drivable area and road anomaly detection, have been developed. However, the detection accuracy still needs improvement. Therefore, we develop a novel module named the Normal Inference Module (NIM), which can generate surface normal information from dense depth images with high accuracy and efficiency. Our NIM can be deployed in existing convolutional neural networks (CNNs) to refine the segmentation performance. To evaluate the effectiveness and robustness of our NIM, we embed it in twelve state-of-the-art CNNs. The experimental results illustrate that our NIM can greatly improve the performance of the CNNs for drivable area and road anomaly detection. Furthermore, our proposed NIM-RTFNet ranks 8th on the KITTI road benchmark and exhibits a real-time inference speed.