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..
EGGS: Exchangeable 2D/3D Gaussian Splatting for Geometry-Appearance Balanced Novel View Synthesis
Authors: Yancheng Zhang, Guangyu Sun, Chen Chen
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that EGGS outperforms existing methods in rendering quality, geometric accuracy, and efficiency, providing a practical solution for high-quality NVS. |
| Researcher Affiliation | Academia | Yancheng Zhang , Guangyu Sun , and Chen Chen Institute of Artificial Intelligence, University of Central Florida EMAIL |
| Pseudocode | Yes | Algorithm 1: Frequency-Decoupled Optimization |
| Open Source Code | Yes | Code and demo available at https://github.com/Fobow/EGGS. |
| Open Datasets | Yes | We evaluate EGGS on several widely used benchmarks. For appearance evaluation, we use Mip-Ne RF360 [25], LLFF [41], Tanks&Temples [42], and DTU [43]. |
| Dataset Splits | Yes | We follow the standard train/test splits used in prior work [25], with additional dataset statistics provided in Table 6. |
| Hardware Specification | Yes | All experiments are conducted on an A5000 GPU. |
| Software Dependencies | No | We implement our hybrid rasterizer based on the CUDA rasterization code of 3DGS [11]. We used the Haar filter for the DWT [37, 48]. The paper mentions software components like CUDA and Haar filter, but does not provide specific version numbers for key software dependencies like Python, PyTorch, or CUDA toolkit versions. |
| Experiment Setup | Yes | Our training setup closely follows 3DGS [11]. All methods, including EGGS and baselines, are trained for 30K iterations. Learning rates for Gaussian parameters follow the default configurations from 3DGS and 2DGS. We adopt the densification strategy from 3DGS, which refines Gaussian distributions by pruning or duplicating them in underor over-reconstructed regions. Densification begins at iteration 500, ends at iteration 15K, and is performed every 100 iterations. ... During type switching, we set the effective rank threshold θe to 2.05. For scale modulation, ... we use θz = 1.05 and temperature T = 0.001 in Eq. 7. Additionally, we incorporate frequency-based supervision... we set the weight for the frequency components as λlow = 0.2 and λlow = 0.4. |