# GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion

arXiv: https://arxiv.org/abs/2609.36844 · HTML: https://mlsanity.com/paper/2609.36844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Enhanced Boundary Learning for Glass-like Object Segmentation](https://mlsanity.com/paper/2103.15734/) (2021)
- [ClearPose: Large-scale Transparent Object Dataset and Benchmark](https://mlsanity.com/paper/2203.03890/) (2022)
- [Learning Depth Estimation for Transparent and Mirror Surfaces](https://mlsanity.com/paper/2307.15052/) (2023)
- [Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World](https://mlsanity.com/paper/2107.03172/) (2021)
- [Glass Segmentation with RGB-Thermal Image Pairs](https://mlsanity.com/paper/2204.05453/) (2022)
- [TRansPose: Large-Scale Multispectral Dataset for Transparent Object](https://mlsanity.com/paper/2307.05016/) (2023)
- [Progressive Glass Segmentation](https://mlsanity.com/paper/2209.02280/) (2022)
- [Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots](https://mlsanity.com/paper/2508.01639/) (2025)
- [Glass Segmentation with Multi Scales and Primary Prediction Guiding](https://mlsanity.com/paper/2402.08571/) (2024)
- [FuseGrasp: Radar-Camera Fusion for Robotic Grasping of Transparent Objects](https://mlsanity.com/paper/2502.20037/) (2025)
- [Real-Time Glass Detection and Reprojection using Sensor Fusion Onboard Aerial Robots](https://mlsanity.com/paper/2510.06518/) (2025)

## Abstract (arXiv)

Transparent surfaces are ubiquitous in built environments, yet they remain a persistent failure case for robotic perception. RGB cameras perceive the background behind glass rather than the surface itself, while depth sensors such as LiDAR, time-of-flight, and RGB-D often return invalid or background measurements in transparent regions. As a result, systems that rely solely on optical sensing may misinterpret glass walls, doors, or mirrors as free space, compromising safe and reliable navigation. Existing glass segmentation approaches address this by learning visual cues such as reflections, boundaries, and semantic context from RGB images. While effective under favourable lighting and viewing conditions, these cues degrade in low-light environments, under glare, or when glass surfaces are featureless or partially occluded. In this work, we propose a multimodal framework that fuses millimetre-wave radar with RGB-D sensing for real-time transparent surface segmentation. Radar reflects strongly off glass surfaces, providing a geometric cue that remains reliable precisely where vision and depth fail. We exploit this cross-modal inconsistency to generate a radar-guided spatial prior, which is integrated into a lightweight transformer-based segmentation network, GlassFormer, via cross-modal attention. We report results on a mixed-condition test split covering all scene types and a dedicated low-light split designed to stress vision-only methods. GlassFormer achieves 0.88 mIoU on the mixed split, and 0.59 mIoU on the low light split, demonstrating substantial robustness gains over vision-only baselines while maintaining real-time performance on resource-constrained platforms.