SILICA is a unified, single-step latent regression model that repurposes text-to-image diffusion priors for joint glass segmentation and glass-aware monocular depth estimation.

Using task-specific CLIP text prompts injected into cross-attention layers, SILICA establishes a spatial hierarchy that resolves foreground-background visual ambiguities inherent to transparent surfaces.

This work has been accepted for publication at IROS 2026.

For setup instructions, Python standalone inference, environment setup via uv/pip, and ROS2 (Humble) integration, please refer directly to the Official GitHub Repository.