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].
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. |