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..
PAPR: Proximity Attention Point Rendering
Authors: Yanshu Zhang, Shichong Peng, Alireza Moazeni, Ke Li
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on both synthetic and real-world datasets demonstrate that PAPR outperforms prior point-based methods in terms of image quality when using a parsimonious set of points. Table 1 shows the average image quality metric scores. PAPR consistently outperforms the baselines across all metrics in both synthetic and real-world settings, without relying on specific initialization. |
| Researcher Affiliation | Academia | Yanshu Zhang , Shichong Peng , Alireza Moazeni, Ke Li APEX Lab School of Computing Science Simon Fraser University EMAIL |
| Pseudocode | Yes | Algorithm 1 Conditional IMLE Training Procedure |
| Open Source Code | Yes | More results and code are available on our project website. |
| Open Datasets | Yes | For the synthetic setting, we choose the Ne RF Synthetic dataset [22], while for the real-world setting, we use the Tanks & Temples [13] subset, following the same data pre-processing steps as in [48]. |
| Dataset Splits | Yes | For the synthetic setting, we choose the Ne RF Synthetic dataset [22], while for the real-world setting, we use the Tanks & Temples [13] subset, following the same data pre-processing steps as in [48]. |
| Hardware Specification | Yes | We train our model using Adam optimizer [12] on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions software components like Adam optimizer, U-Net architecture, and LPIPS metric, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set the weight λ to 0.01 for all experiments. During training, we jointly optimize all model parameters, including pi, ui, τi, θK, θV , θQ and θR. We train our model using Adam optimizer [12] on a single NVIDIA A100 GPU. We set the parameter K = 20 for selecting the top nearest points, and the point feature vector dimension h = 64. All influence scores are initialized to zero. Starting from iteration 10, 000, we prune points with τi < 0 every 500 iterations. |