TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
Authors: Aljaz Bozic, Pablo Palafox, Justus Thies, Angela Dai, Matthias Niessner
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments Metrics. To evaluate our monocular scene reconstruction, we use several measures of reconstruction performance. Table 1: Quantitative comparison with baselines and ablations on test set of Scannet dataset [8]. |
| Researcher Affiliation | Academia | 1Technical University of Munich 2Max Planck Institute for Intelligent Systems, Tübingen, Germany |
| Pseudocode | No | The paper describes the method in text and diagrams (Figure 2) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper includes a personal project URL (aljazbozic.github.io/transformerfusion) but does not contain an explicit statement about the release of source code for the methodology or a direct link to a code repository. |
| Open Datasets | Yes | To train our approach we use Scan Net dataset [8], an RGB-D dataset of indoor apartments. |
| Dataset Splits | Yes | We follow the established train-val-test split. |
| Hardware Specification | Yes | Training takes about 30 hours using an Intel Xeon 6242R Processor and an Nvidia RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch library [31]' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | During training, a batch size of 4 chunks is used with an Adam [23] optimizer with β1 = 0.9, β2 = 0.999, ϵ = 10 8 and weight regularization of 10 4. We use a learning rate of 10 4 with 5k warm-up steps at initialization, and square root learning rate decay afterwards. When computing the losses of coarse and fine surface filtering predictions, a higher weight of 2.0 is applied to near-surface voxels, to increase recall and improve overall robustness. |