From Pixels to Depth: My Mini Adventure with the RGB-D Freiburg Dataset

"What if I told you your webcam could see depth like your eyes?"Let’s talk about how I explored one such dataset that makes it possible — and had some fun debugging along the way 😅.

🎯 Goal

I wanted to load and display pairs of RGB and depth images from a public RGB-D dataset. Specifically, I used the well-known: rgbd_dataset_freiburg1_xyz — from the TUM RGB-D benchmark.

The idea? 👉 To visualize the color image and its depth map side by side like this:

🗃️ The Dataset

• A bunch of RGB images
• Corresponding depth images
• A few timestamp files like rgb.txt and depth.txt

But they weren’t directly linked — so, I had to create a new file associations.txt to link each RGB with its depth frame by comparing timestamps. This is typical in SLAM or robotics data.

🧩 My Approach

1. Parsed timestamp files
2. Matched RGB and depth timestamps if they were within 0.02 seconds
3. Saved those pairs to associations.txt
4. Loaded and visualized the first pair using OpenCV & matplotlib

Yes, it took some trial and error — I even forgot to write to associations.txt at first, so my list was empty 😅 Lesson learned: Always check if your list is populated before using list[0]!

🛠️ Tools & Skills Used

• OpenCV – for reading and displaying images
• Matplotlib – for side-by-side plots
• Python File Parsing – for processing timestamp files
• Debugging – for surviving the “empty list” trap
• Basic SLAM knowledge – for pairing RGB-D frames.

💡 What I Learned

• How to work with RGB-D datasets
• Importance of robust timestamp matching
• Realized how powerful a simple depth map can be when visualized next to the RGB image
• A reminder that data preprocessing is as important as model building

💬 Final Thoughts

If you're stepping into Computer Vision, SLAM, or just want to build cool stuff with RGB-D data, this is a great way to get your hands dirty.

💻 Want to explore the code?I’ve shared the notebook on GitHub with comments and visual output.🔗 github.com/Harisha25/rgb-depth-pairing

#Python #ComputerVision #OpenCV #Robotics #SLAM #RGBD #DepthSensing #LinkedInBlog #TUMDataset #Matplotlib #DebuggingWins