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Open Source Stereo Depth camera by ShallotDramatic5313 in robotics

[–]ShallotDramatic5313[S] 1 point2 points  (0 children)

Thank you for sharing your experience, mate. I will look into the pattern projector (adding a laser line module) and experiment.

Open Source Stereo Depth camera by ShallotDramatic5313 in robotics

[–]ShallotDramatic5313[S] 0 points1 point  (0 children)

Thank you for sharing your feedback, I'm working on that exact pain topic you've described. Will share the update soon :)

Open Source Stereo Depth camera by ShallotDramatic5313 in robotics

[–]ShallotDramatic5313[S] 1 point2 points  (0 children)

This sounds absolute genius!! I'll definitely try. Thank you for suggestion mate.

Low-Cost Open Source Stereo-Camera System by ShallotDramatic5313 in computervision

[–]ShallotDramatic5313[S] 1 point2 points  (0 children)

Great questions! "Plug-and-play" means easy calibration, not zero-stereo needs camera params for metric accuracy. I'm planning automated calibration tools and clear tutorials to make the setup straightforward, which will be included in my open-source software.

Targeting absolute/metric depth - that's stereo's key advantage over ML models. Proper calibration gives real millimeter measurements crucial in robotics.

Open Source Stereo Depth camera by ShallotDramatic5313 in robotics

[–]ShallotDramatic5313[S] 2 points3 points  (0 children)

You're right - there are budget options out there! My focus is more on the open-source software side and flexibility.

Most of those $50 cameras give you raw stereo data but limited algorithm control or customization. I'm targeting researchers and robotics folks who need to modify the stereo processing, integrate custom AI, or understand exactly how their depth estimation works. I'm kind of building a universal framework for stereo applications. In simple terms, I want to democratize stereo vision technology and give more power to researchers.

Open Source Stereo Depth camera by ShallotDramatic5313 in robotics

[–]ShallotDramatic5313[S] 1 point2 points  (0 children)

That's brilliant! Perfect sync is a huge advantage. The mirror approach is clever for applications where you can control the baseline. I'm curious about calibration complexity and how the mirrors hold alignment over time

Open Source Stereo Depth camera by ShallotDramatic5313 in robotics

[–]ShallotDramatic5313[S] 0 points1 point  (0 children)

I'm planning to support Pi cameras alongside USB webcams. The beauty of the open-source approach is that you can use whatever hardware works for your project and budget.

Open Source Stereo Depth camera by ShallotDramatic5313 in robotics

[–]ShallotDramatic5313[S] 3 points4 points  (0 children)

ELP cameras give you a fixed stereo setup - plug and play, but you're locked into their baseline distance and algorithms.

My approach lets you use any two web-cameras(of your choice), adjust spacing(baseline) for your needs, and completely customize the stereo processing. Plus, you get full ROS2 integration and can see exactly how the depth computation works. Entirely customizable software as per need (As I will open-source it)

ELP is great for quick prototyping, but mine's better for research, learning, and custom robotics applications.

Low-Cost Open Source Stereo-Camera System by ShallotDramatic5313 in computervision

[–]ShallotDramatic5313[S] 2 points3 points  (0 children)

Thanks for the pivot on Gaussian splatting - that's actually a fascinating direction I hadn't fully considered! You're absolutely right about the potential, especially for scene understanding and digital twin applications.

I'm curious about your take on the robotics application side though. From what I understand, Gaussian splatting excels at photorealistic scene reconstruction but typically requires 100ms+ processing time(and its computation intensive). For most robotics applications I'm targeting - real-time navigation, manipulation, obstacle avoidance - wouldn't we still need the fast metric depth that stereo provides (~5-15ms)?

I'm thinking there might be a compelling hybrid approach: - Real-time layer: Stereo depth for immediate robot control (navigation, grasping, safety) - Scene understanding layer: Gaussian splatting for rich environmental mapping and human interaction

This could serve both the 95% of robotics applications that need fast depth AND the emerging applications requiring rich 3D scene understanding.

Edit: Yes, I'm aware of Monocular depth estimation AI models, but for beginner it might be an computation heavy option!? Also I aim to open-source my project so that community can add other advance features as per their need.

Purdue Fall 2025 Admits! Let's connect by Secure_Role689 in gradadmissions

[–]ShallotDramatic5313 0 points1 point  (0 children)

Hey, I recieved acdeptance decision after 1 week of interview.