Gaussian Splat VS Single Image 3D Model Generation

Описание: I’ve been super interested in the idea of turning 2D images or video into 3D models for a while now. And of course with AI, everything seems to be getting better and faster. I started experimenting with Gaussian splats when the process first became available a couple of years ago, and since then, I’ve been exploring other methods too, like generating 3D models from a single image.

Recently, I ran a fun little test to compare both approaches using the same subject: a super-stylized tractor I spotted at a park. Reminded me of something out of the Roger Rabbit or Cars movies, so it seemed like a great object to experiment with!

For the Gaussian splat version, I used LumaLabs. It did a decent job capturing the overall shape of the tractor, but the geometry came out a bit low-res and bumpy in areas that should be smooth. There were also a few holes in the mesh, so it wasn’t watertight, which means it would need some cleanup if you wanted to 3D print it.

For the single image to 3D model test, I used Sparc3D. The geometry here was noticeably higher in resolution and much cleaner, and it seemed to mirror the left and right sides of the tractor perfectly. It even captured small details like recessed lines and subtle surface shapes. Despite only seeing the front and side, the process assumed and created some of the backside and even generated a partial steering wheel area. The mesh was also watertight with no cleanup required.

In terms of texture quality, both methods captured the color pretty well, though still on the lower resolution side. The models would likely hold up as background elements in a game, TV show, or movie if composited correctly.

Overall, both processes were surprisingly easy to use, almost too easy! Of course, I’m not the original designer of the tractor, that credit belongs to whoever created it in the real world, but testing out these tools was a fun way to see how different AI techniques interpret and reconstruct the same object.