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 [1].
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. |