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..
UP-NeRF: Unconstrained Pose Prior-Free Neural Radiance Field
Authors: Injae Kim, Minhyuk Choi, Hyunwoo J. Kim
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments verify the superior performance of our method compared to the baselines including BARF and its variants in a challenging internet photo collection, Phototourism dataset. |
| Researcher Affiliation | Academia | Injae Kim Korea University EMAIL Minhyuk Choi Korea University EMAIL Hyunwoo J. Kim Korea University EMAIL |
| Pseudocode | No | The paper describes its methods using mathematical equations and textual explanations, but it does not include a formally labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | The code of UP-Ne RF is available at https://github.com/mlvlab/UP-Ne RF. |
| Open Datasets | Yes | We report results on the Phototourism dataset. It consists of internet photo collections of famous landmarks and we select 4 scenes, Brandenburg Gate, Sacre Coeur, Taj Mahal, and Trevi fountain, which are also used in Ne RF-W. |
| Dataset Splits | Yes | We follow the split used by Ne RF-W [18] and downsample each image by 2 times. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The paper mentions using specific software components like 'Adam optimizer', 'Adam W', 'Vi T', 'DPT', and 'DINO model' but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | All the models are trained for 600K iterations with randomly sampled 2048 pixel rays at each step with a learning rate of 5 10 4 decaying to 5 10 5 for Ne RF and transient network T , and 2 10 3 decaying to 1 10 3 for pose p and two factors si and ti of depth. We use Adam optimizer [51] across all the experiments except test-time appearance optimization, where Adam W [52] is used instead. The number of sampling points in each ray for volumetric rendering is set to 128 for both coarse and fine models. We use the default coarse-to-fine strategy of BARF which starts from training progress 0.1 to 0.5. We set the scheduling parameters u and v to be 0.1 and 0.5, respectively, the same as the parameters of coarse-to-fine. The hyperparameter λd for depth loss is set to 0.001. We set βmin to 0.1 and λα to 1.0. |