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..
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
Authors: Aljaz Bozic, Pablo Palafox, Justus Thies, Angela Dai, Matthias Niessner
NeurIPS 2021 | Venue PDF | 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. |