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..
WildGaussians: 3D Gaussian Splatting In the Wild
Authors: Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Marc Pollefeys, Torsten Sattler
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our Wild Gaussians approach on two challenging datasets. The Ne RF On-the-go dataset [31] contains multiple casually captured indoor and outdoor sequences, with varying ratios of occlusions (from 5% to 30%). |
| Researcher Affiliation | Academia | 1 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague 2 Faculty of Electrical Engineering, Czech Technical University in Prague 3 Department of Computer Science, ETH Zurich 4 Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague |
| Pseudocode | No | The paper describes algorithms but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code, model checkpoints, and video comparisons are available at: https://wild-gaussians.github.io/ |
| Open Datasets | Yes | We evaluate our Wild Gaussians approach on two challenging datasets. The Ne RF On-the-go dataset [31] contains multiple casually captured indoor and outdoor sequences, with varying ratios of occlusions (from 5% to 30%). [...] The Photo Tourism dataset [35] consists of multiple 3D scenes of well-known monuments. [...] The Ne RF On-the-go dataset is licensed under the Apache 2.0 license (https://raw.githubusercontent.com/cvg/nerf-on-the-go/refs/heads/master/LICENSE). The pictures in the Photo Tourism dataset were sourced from various creators who made the images available under permissive licenses (https://creativecommons.org/share-your-work/cclicenses/). |
| Dataset Splits | No | The paper describes training steps and evaluation protocols but does not provide specific percentages or counts for training, validation, and test dataset splits for reproduction. |
| Hardware Specification | Yes | All our experiments were conducted on a single NVIDIA RTX 4090 GPU. [...] Methods were trained and evaluated on NVIDIA A100, while the rest used NVIDIA GTX 4090. |
| Software Dependencies | No | The paper mentions software components like 3DGS renderer, Mip-Splatting, Gaussian Opacity Fields, DINO v2, and Adam optimizer, but it does not specify their version numbers. |
| Experiment Setup | Yes | We optimize each representation for 30k training steps. For our choice of learning rates, we mostly follow 3DGS [14]. We differ in the following learning rates: appearance MLP lr. of 0.0005, uncertainty lr. of 0.001. Gaussian embedding lr. of 0.005. image embedding lr. of 0.001. For the position learning rate, we exponentially decay from 1.6 10 4 to 1.6 10 6. Furthermore, we set the densification threshold at 0.0002 and density from 500-th iteration to 15 000th iteration every 100 steps. Furthermore, we reset the opacity every 3 000 steps. We do not optimize the uncertainty predictor for 500 after each opacity reset, and we do not apply the masking for the first 2000 training steps. |