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..
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
Authors: Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, Fredo Durand
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of LFNs by reconstructing 360-degree light fields of a variety of simple 3D scenes. In all experiments, we parameterize LFNs via a 6-layer Re LU MLP, and the hypernetwork as a 3-layer Re LU MLP, both with layer normalization. We solve all optimization problems using the ADAM solver with a step size of 10 4. Please find more results, as well as precise hyperparameter, implementation, and dataset details, in the supplemental document and video. Table 1: Single-shot multi-class reconstruction results. We benchmark LFNs with SRNs [3] and DVR [5] on the task of single-shot (auto-decoding with a single view), multi-class reconstruction of the 13 largest Shape Net [59] classes. |
| Researcher Affiliation | Academia | Vincent Sitzmann1, EMAIL Semon Rezchikov2, EMAIL William T. Freeman1,3 EMAIL Joshua B. Tenenbaum1,4,5 EMAIL Frédo Durand1 EMAIL 1MIT CSAIL 2Columbia University 3 IAFI 4MIT BCS 5 CBMM |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'please see the code for implementation details' and refers to a project page 'vsitzmann.github.io/lfns/' and 'supplemental document and video', but does not provide a concrete access link to a source code repository or an explicit statement of code release for the methodology described within the main paper. |
| Open Datasets | Yes | We train LFNs on the Shape Net cars dataset with 50 observations per object from [3], as well as on simple room-scale environments as proposed in [13]. ShapeNet: An information-rich 3d model repository. ar Xiv preprint ar Xiv:1512.03012, 2015. |
| Dataset Splits | Yes | Multi-class single-view reconstruction. Following [5, 6], we benchmark LFNs with recent global conditioning methods on the task of single-view reconstruction and novel view synthesis of the 13 largest Shape Net categories. We follow the same evaluation protocol as [60] and train a single model across all categories. |
| Hardware Specification | Yes | All clock times were collected for rendering 256 256 images on an NVIDIA RTX 6000 GPU. |
| Software Dependencies | No | The paper mentions using the 'ADAM solver' but does not provide specific software names with version numbers for libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | In all experiments, we parameterize LFNs via a 6-layer Re LU MLP, and the hypernetwork as a 3-layer Re LU MLP, both with layer normalization. We solve all optimization problems using the ADAM solver with a step size of 10 4. |