Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Anti-Aliased 2D Gaussian Splatting
Authors: Mae Younes, Adnane Boukhayma
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate AA-2DGS on standard novel view synthesis datasets, including Mip-Ne RF 360 [2] and Blender [43], as well as the DTU [29] mesh reconstruction benchmark. Our results demonstrate that AA-2DGS consistently outperforms the original 2DGS method, particularly under challenging conditions such as varying sampling rates and mixed resolution training. |
| Researcher Affiliation | Academia | Mae Younes Adnane Boukhayma INRIA France, University of Rennes, CNRS, IRISA |
| Pseudocode | No | The paper describes methods using mathematical formulations and descriptive text, but no explicit pseudocode or algorithm block is present. |
| Open Source Code | No | Code will be available at AA-2DGS. |
| Open Datasets | Yes | We evaluate AA-2DGS on standard novel view synthesis datasets, including Mip-Ne RF 360 [2] and Blender [43], as well as the DTU [29] mesh reconstruction benchmark. |
| Dataset Splits | Yes | The Blender dataset [43] includes 8 synthetically rendered scenes... Each scene has 100 training views and 200 test views, rendered at 800 x 800 resolution. |
| Hardware Specification | Yes | We conduct all experiments on NVIDIA RTX A6000 GPUs. |
| Software Dependencies | No | The paper mentions 'custom CUDA kernels' for implementation but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We train our models for 30K iterations across all scenes and use the same loss function, Gaussian density control strategy, schedule, and hyperparameters. ... We follow the Mip-Splatting approach and recompute the sampling rate of each 2D Gaussian primitive every m = 100 iterations. Similarly, we choose the variance of our object-space Mip filter as 0.1, approximating a single pixel, and the variance of the flat smoothing filter as 0.2. |