Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
Authors: Vincent Sitzmann, Michael Zollhoefer, Gordon Wetzstein
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train SRNs on several object classes and evaluate them for novel view synthesis and few-shot reconstruction. We further demonstrate the discovery of a non-rigid face model. |
| Researcher Affiliation | Academia | Vincent Sitzmann Michael Zollhöfer Gordon Wetzstein {sitzmann, zollhoefer}@cs.stanford.edu, gordon.wetzstein@stanford.edu Stanford University |
| Pseudocode | Yes | Algorithm 1 Differentiable Ray-Marching |
| Open Source Code | Yes | Code and datasets are available. |
| Open Datasets | Yes | Shapenet v2. We consider the chair and car classes of Shapenet v.2 [39] with 4.5k and 2.5k model instances respectively. |
| Dataset Splits | No | The paper explicitly defines training and test sets (e.g., 'training set', 'held-out test set', '50 images (training set)'). However, it does not explicitly state the use of a validation set or specific splits for validation, nor does it provide exact split percentages or counts for training and testing beyond '50 images of each object' for training and '100 objects from a held-out test set'. |
| Hardware Specification | No | A single forward pass takes around 120 ms and 3 GB of GPU memory per batch item. |
| Software Dependencies | No | The paper mentions 'Hyperparameters, computational complexity, and full network architectures for SRNs and all baselines are in the supplement.', but it does not specify software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | Yes | Hyperparameters, computational complexity, and full network architectures for SRNs and all baselines are in the supplement. |