PAPR: Proximity Attention Point Rendering
Authors: Yanshu Zhang, Shichong Peng, Alireza Moazeni, Ke Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 {yanshu_zhang,shichong_peng,seyed_alireza_moazenipourasil,keli}@sfu.ca |
| 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. |