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

arXiv:2609.36844 · cs.CV, cs.RO · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/Suhani92/GlassFormer

Terminology

• Transformer — 10× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 4× in this paper
• Mask Generation — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Glass Segmentation with Multi Scales and Primary Prediction Guiding
• ClearPose: Large-scale Transparent Object Dataset and Benchmark
• TRansPose: Large-Scale Multispectral Dataset for Transparent Object
• Segmenting Transparent Object in the Wild with Transformer
• Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection
• Learning Depth Estimation for Transparent and Mirror Surfaces
• Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots
• FuseGrasp: Radar-Camera Fusion for Robotic Grasping of Transparent Objects
• Real-Time Glass Detection and Reprojection using Sensor Fusion Onboard Aerial Robots
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

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