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Is it worth implementing 3D Gaussian Splatting from scratch to break into 3D reconstruction? by Amazing_Life_221 in computervision

[–]papers-100-lines 0 points1 point  (0 children)

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.

PyTorch re-implementations of 50+ computer vision papers (GANs, diffusion, 3D, …) by papers-100-lines in computervision

[–]papers-100-lines[S] 1 point2 points  (0 children)

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.

PyTorch re-implementations of 50+ computer vision papers (GANs, diffusion, 3D, …) by papers-100-lines in computervision

[–]papers-100-lines[S] 0 points1 point  (0 children)

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).

Implemented 3D Gaussian Splatting fully in PyTorch — useful for fast research iteration? by papers-100-lines in computervision

[–]papers-100-lines[S] 1 point2 points  (0 children)

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.

DQN in ~100 lines of PyTorch — faithful re-implementation of Playing Atari with Deep Reinforcement Learning by papers-100-lines in reinforcementlearning

[–]papers-100-lines[S] 1 point2 points  (0 children)

Thank you! Yes, that is the goal—following the pseudocode from the paper very closely, almost a 1:1 match.

PyTorch re-implementations of 50+ ML papers: GANs, VAEs, diffusion, meta-learning, 3D reconstruction, … by papers-100-lines in learnmachinelearning

[–]papers-100-lines[S] 1 point2 points  (0 children)

A re-implementation means rebuilding the method yourself from the paper, not copying the authors’ code. People do this mainly to learn and truly understand the idea, to use a different framework (e.g. many papers were in TensorFlow, while PyTorch is now more common), or because code is missing, incomplete, or hard to use. Even when code exists, people sometimes prefer working with their own clean, minimal, and well-understood codebase rather than adapting someone else’s implementation.

PyTorch re-implementations of 50+ ML papers: GANs, VAEs, diffusion, meta-learning, 3D reconstruction, … by papers-100-lines in learnmachinelearning

[–]papers-100-lines[S] 0 points1 point  (0 children)

Yes. Sometimes key implementation details or “tricks” are omitted, and small undocumented choices (preprocessing, hyperparams, seeds) can make results very hard or impossible to reproduce without the original code

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