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..
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Authors: Bipasha Sen, Gaurav Singh, Aditya Agarwal, Rohith Agaram, Madhava Krishna, Srinath Sridhar
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide qualitative comparisons and evaluate Hy P-Ne RF on three tasks: generalization, compression, and retrieval, demonstrating our state-of-the-art results. |
| Researcher Affiliation | Academia | Bipasha Sen MIT CSAIL EMAIL Gaurav Singh IIIT, Hyderabad gaurav.si Aditya Agarwal MIT CSAIL EMAIL Rohith Agaram IIIT, Hyderabad rohith.agaram K Madhava Krishna IIIT, Hyderabad EMAIL Srinath Sridhar Brown University EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It provides mathematical equations and architectural diagrams. |
| Open Source Code | No | The paper mentions a project page 'hyp-nerf.github.io' in a footnote, which typically showcases results but does not guarantee the release of source code. It also cites '[1] Git Hub ashawkey/torch-ngp', which is a third-party implementation of Instant NGP used as a component, not the authors' full Hy P-Ne RF source code. There is no explicit statement about their own source code being released. |
| Open Datasets | Yes | We primarily compare against two baselines, Pixel Ne RF [75] and Instant NGP [35] on the Amazon-Berkeley Objects (ABO) [11] dataset. [...] Additionally, we compare with the other baselines on SRN at 128 128 resolution qualitatively in the main paper (Figure 5) and quantitatively in the supplementary. |
| Dataset Splits | No | The paper mentions a 'training dataset' and uses 'novel Ne RF instances' for generalization testing, but it does not specify a distinct validation set or its size/percentage for hyperparameter tuning or model selection during training. |
| Hardware Specification | Yes | We perform all of our experiments on NVIDIA RTX 2080Tis. |
| Software Dependencies | No | The paper mentions using 'Instant NGP' and 'VQVAE2 [42] as the backbone' but does not specify any version numbers for these software dependencies or any other libraries. |
| Experiment Setup | Yes | We use Instant NGP as f( )n, with 16 levels, hashtable size of 211, feature dimension of 2, and linear interpolation for computing the MRHE; the MLP has a total of 5, 64-dimensional, layers. Our hypernetwork, M, consists of 6 MLPs, 1 for predicting the MRHE, and the rest predicts the parameters ϕ for each of the MLP layers of f. Each of the MLPs are made of 3, 512-dimensional, layers. |