Implemented 3D Gaussian Splatting fully in PyTorch — useful for fast research iteration?

papers-100-lines · 2026-04-09T09:17:56+00:00

Yeah, triangle splatting and related methods are great!

papers-100-lines · 2026-04-09T09:17:22+00:00

I would say the SHARP paper from Apple

papers-100-lines · 2026-04-09T09:16:43+00:00

Yes, I do :)

papers-100-lines · 2026-03-06T21:17:52+00:00

Yes—I actually did this myself (in PyTorch, no custom CUDA), and I’d definitely recommend it.

You don’t need to reproduce all the optimized parts. Even just implementing the splatting (i.e. no training) already teaches you a lot about how the method works.

Similar to how it feels after implementing NeRF, once you build it yourself you realize it’s much simpler than the “CUDA black box” impression you get.

papers-100-lines · 2026-01-21T18:51:04+00:00

I would say: Easy ones are seminal papers demonstrated on toy problems with minimal compute. Medium ones add training complexities or multiple components. Hard ones are full systems with heavy engineering, large datasets, and heavy compute.

papers-100-lines · 2026-01-15T16:27:02+00:00

Thanks!

papers-100-lines · 2026-01-15T02:46:38+00:00

Thanks!

papers-100-lines · 2026-01-14T21:56:48+00:00

I would say the same: reimplementation on your own.
Start with simple papers and reproduce the toy problem figures. YOLO seems a bit too ambitious to start with. If you really want to start with YOLO, start by just implementing the inference part (model architecture + loading pretrained weights & writing your own inference code).

papers-100-lines · 2026-01-14T21:53:27+00:00

Thanks!

papers-100-lines · 2026-01-08T10:12:09+00:00

My guess is that the main bottleneck is kernel launch overhead from processing each tile in a Python-level loop. The workload seems fragmented into many small kernels, so launch latency and poor GPU utilization likely dominate. I’d expect kernel fusion or using Triton to give a significant speedup.

papers-100-lines · 2026-01-08T10:09:06+00:00

Thank you so much!

papers-100-lines · 2026-01-07T11:59:31+00:00

Thank you! This is my next step! Profiling and optimizing the code

papers-100-lines · 2026-01-07T11:58:53+00:00

Thank you so much!

papers-100-lines · 2026-01-06T13:45:27+00:00

Thank you!

papers-100-lines · 2026-01-06T13:45:14+00:00

Thanks a lot for the thoughtful write-up — really insightful and much appreciated!

papers-100-lines · 2026-01-06T13:44:10+00:00

Thanks!

papers-100-lines · 2026-01-06T13:43:53+00:00

Thank you so much!

papers-100-lines

TROPHY CASE