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..
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation
Authors: Yuhan Ding, Fukun Yin, Jiayuan Fan, Hui Li, Xin Chen, Wen Liu, Chongshan Lu, Gang Yu, Tao Chen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have demonstrated the effectiveness of our method for large-scale scene novel view synthesis, which outperforms relevant state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1 School of Information Science and Technology, Fudan University, China 2 Academy for Engineering and Technology, Fudan University, China 3 Tencent PCG, Shanghai, China |
| Pseudocode | No | The paper describes methods and processes in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our code and models will be available. |
| Open Datasets | Yes | We use two outdoor large-scale scene datasets, OMMO [15] and Blended MVS [35], to evaluate our model. |
| Dataset Splits | No | The paper mentions 'training views' and 'training data' but does not specify explicit percentages or sample counts for training, validation, and test dataset splits. |
| Hardware Specification | Yes | train on 4 A100 GPUs for around one day" and "20 hours on a single A100 GPU |
| Software Dependencies | No | The paper mentions software like COLMAP and Adam optimizer but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For point super-resolution diffusion, we set T = 1000, β0 = 10 4, βT = 0.01 and linearly interpolate other β s for all experiments. We use Adam optimizer with learning rate 2 10 4 and train on 4 A100 GPUs for around one day. We train this stage using Adam optimizer with an initial learning rate 5 10 4 for 2 106 iterations about 20 hours on a single A100 GPU. |