arXiv Computer Vision
SPLATIFY: Reproduce, Discover, Innovate! From Papers and Ideas to Trainable 3DGS Code
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SplatLab is an autonomous research system designed for 3D Gaussian Splatting (3DGS) that can reproduce published baselines, propose extensions, train models, and generate manuscripts with traceable results. It uses a verified-reproduction backbone, a cascade-weighted Elo tournament for candidate evaluation, a standing falsifier for stress‑test survival, and a seven‑category 3DGS taxonomy to ensure reproducibility and transferability. In PSNR‑targeted experiments, SplatLab’s manuscripts outperform their reproduced baselines by up to +3.78 dB, compressing the research cycle from months to days of autonomous compute.
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