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..
DäRF: Boosting Radiance Fields from Sparse Input Views with Monocular Depth Adaptation
Authors: Jiuhn Song, Seonghoon Park, Honggyu An, Seokju Cho, Min-Seop Kwak, Sungjin Cho, Seungryong Kim
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show our framework achieves state-of-the-art results both quantitatively and qualitatively, demonstrating consistent and reliable performance in both indoor and outdoor real-world datasets. We evaluate and compare our approach on real-world indoor and outdoor scene datasets, establishing new state-of-the-art results for the benchmarks. |
| Researcher Affiliation | Academia | Korea University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and pre-trained weights will be made publicly available. |
| Open Datasets | Yes | Following previous works [37, 45], we use a subset of sparse-view Scan Net data [10] comprised with three indoor scenes... For outdoor reconstruction, we further test on 5 challenging scenes from the Tanks and Temples dataset [19]. |
| Dataset Splits | No | The paper mentions "training images" and "test images" (e.g., "18 to 20 training images and 8 test images") but does not explicitly provide details about a validation dataset split or how it was used for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions several software components like "K-planes [32] as Ne RF", "DPT-hybrid [34] as MDE model", and "Adam [18] as an optimizer," but it does not specify their version numbers or other ancillary software details needed for reproduction. |
| Experiment Setup | Yes | We use Adam [18] as an optimizer, with a learning rate of 1 10 2 for Ne RF and 1 10 5 for the MDE, along with a cosine warmup learning rate scheduling. |