# LiDAR guided Small obstacle Segmentation

arXiv: https://arxiv.org/abs/2003.05970 · HTML: https://mlsanity.com/paper/2003.05970/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation](https://mlsanity.com/paper/2104.14812/) (2021)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Unmasking Anomalies in Road-Scene Segmentation](https://mlsanity.com/paper/2307.13316/) (2023)
- [HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection](https://mlsanity.com/paper/2206.15157/) (2022)
- [UGainS: Uncertainty Guided Anomaly Instance Segmentation](https://mlsanity.com/paper/2308.02046/) (2023)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving](https://mlsanity.com/paper/2505.02148/) (2025)
- [UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving](https://mlsanity.com/paper/2512.09864/) (2025)
- [Indoor Obstacle Discovery on Reflective Ground via Monocular Camera](https://mlsanity.com/paper/2401.01445/) (2024)
- [OoDIS: Anomaly Instance Segmentation and Detection Benchmark](https://mlsanity.com/paper/2406.11835/) (2024)
- [Detecting and Learning the Unknown in Semantic Segmentation](https://mlsanity.com/paper/2202.08700/) (2022)
- [Road Obstacle Video Segmentation](https://mlsanity.com/paper/2509.13181/) (2025)
- [Evaluation of Multimodal Semantic Segmentation using RGB-D Data](https://mlsanity.com/paper/2103.16758/) (2021)
- [Spatio-Temporal Context Modeling for Road Obstacle Detection](https://mlsanity.com/paper/2301.07921/) (2023)
- [Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation](https://mlsanity.com/paper/2604.23604/) (2026)
- [LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentation](https://mlsanity.com/paper/2606.21527/) (2026)

## Abstract (arXiv)

Detecting small obstacles on the road is critical for autonomous driving. In this paper, we present a method to reliably detect such obstacles through a multi-modal framework of sparse LiDAR(VLP-16) and Monocular vision. LiDAR is employed to provide additional context in the form of confidence maps to monocular segmentation networks. We show significant performance gains when the context is fed as an additional input to monocular semantic segmentation frameworks. We further present a new semantic segmentation dataset to the community, comprising of over 3000 image frames with corresponding LiDAR observations. The images come with pixel-wise annotations of three classes off-road, road, and small obstacle. We stress that precise calibration between LiDAR and camera is crucial for this task and thus propose a novel Hausdorff distance based calibration refinement method over extrinsic parameters. As a first benchmark over this dataset, we report our results with 73% instance detection up to a distance of 50 meters on challenging scenarios. Qualitatively by showcasing accurate segmentation of obstacles less than 15 cms at 50m depth and quantitatively through favourable comparisons vis a vis prior art, we vindicate the method's efficacy. Our project-page and Dataset is hosted at https://small-obstacle-dataset.github.io/