DPSNet: End-to-end Deep Plane Sweep Stereo
Authors: Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Ablation studies indicate that each of these technical contributions leads to appreciable improvements in reconstruction accuracy. |
| Researcher Affiliation | Collaboration | 1 KAIST, 2 Carnegie Mellon University, 3 Microsoft Research Asia |
| Pseudocode | No | The paper describes the pipeline and methods in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an explicit statement about releasing its source code or a direct link to a code repository for the described methodology. |
| Open Datasets | Yes | In the training procedure, we use image sequences, ground-truth depth maps for reference images, and the provided camera poses from public datasets, namely SUN3D, RGBD, and Scenes112. |
| Dataset Splits | No | The paper mentions using datasets for training and testing but does not provide specific details on the train/validation/test splits, such as percentages or sample counts for each subset. |
| Hardware Specification | Yes | The training is performed with a customized version of Py Torch on four NVIDIA 1080Ti GPUs, which usually takes four days. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not specify a version number or other software dependencies with their respective versions. |
| Experiment Setup | Yes | We train our model from scratch for 1200K iterations in total. All models were trained end-to-end with the ADAM optimizer (β1 = 0.9, β2 = 0.999). We use a batch size of 16 and set the learning rate to 2e 4 for all iterations. |