SILICA: Repurposing Diffusion Priors for Joint Glass Segmentation and Depth Estimation

IROS 2026
Tarun R¹, Anuj Verma¹, Laksh Nanwani¹, Sourav Garg¹, Madhava Krishna^1
1 Robotics Research Center (RRC), IIIT Hyderabad, India

Setup

1. Clone the repository.

   git clone https://github.com/rtarun1/Silica

2. Install the required dependencies.

   Using uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   cd Silica
   uv venv .venv
   source .venv/bin/activate
   uv sync

   Using pip:

   cd Silica
   python3 -m venv .venv
   source .venv/bin/activate
   pip install -e .

3. Try SILICA on your images:

   # minimum 5GB VRAM required
   bash infer.sh

ROS2 Setup (Humble)

1. Clone the repository.

   git clone https://github.com/rtarun1/Silica
   
   # or add submodule to your existing workspace
   # git submodule add https://github.com/rtarun1/Silica silica

2. Install the required dependencies.

   Using pip:

   cd Silica
   python3 -m pip install -e .

3. Build the ROS2 workspace:

   cd <your_ros2_workspace>
   colcon build --symlink-install --packages-select silica_ros

4. Source the workspace:

   source <your_ros2_workspace>/install/setup.bash

5. Run the Silica demo launch file:

   ros2 launch silica_ros silica.launch.py

Note

Make sure that you have a camera and that the camera is publishing RGB images. You will also need (aligned) depth images to get the glass-aware point-cloud.

The topics can be set in the config file config/params.yaml.

Roadmap

• Inference code + Mirage18k Dataset
• ROS2 Integration
• Training & Evaluation code