Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
Authors: Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, Fredo Durand
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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, sitzmann@mit.edu Semon Rezchikov2, skr@math.columbia.edu William T. Freeman1,3 billf@mit.edu Joshua B. Tenenbaum1,4,5 jbt@mit.edu Frédo Durand1 fredo@mit.edu 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. |