CURVATURE-GUIDED DYNAMIC SCALE NETWORKS FOR MULTI-VIEW STEREO
Authors: Khang Truong Giang, Soohwan Song, Sungho Jo
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments showed that the proposed method outperforms other methods on complex outdoor scenes. It significantly improves the completeness of reconstructed models. |
| Researcher Affiliation | Academia | a School of Computing, KAIST, Daejeon, 34141, Republic of Korea b Intelligent Robotics Research Division, ETRI, Daejeon 34129, Republic of Korea |
| Pseudocode | No | The paper describes algorithms and methods in text and uses figures to illustrate architectures, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is available at https://github.com/ Truong Khang/cds-mvsnet. |
| Open Datasets | Yes | The datasets used in our evaluation are DTU (Aanæs et al., 2016), Blended MVS (Yao et al., 2020), and Tanks & Temples (Knapitsch et al., 2017). |
| Dataset Splits | Yes | The evaluation of depth map and pointcloud is collected on DTU validation and test set, respectively. |
| Hardware Specification | Yes | The entire network CDS-MVSNet was trained with 30 epochs, using the SGD optimizer with an initial learning rate of 0.01, and on two NVIDIA Titan V GPU with a batch size of 6. |
| Software Dependencies | No | The paper mentions using the 'SGD optimizer' but does not specify versions for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | For CDS-MVSNet, we set the number of input views to 3, the number of depth hypothesis planes to {48, 32, 8} with the corresponding depth interval scales {4, 2, 1}. To train CDFSNet effectively, we used the feature loss as mentioned in Section 4.2. Besides, we implemented the scale selection step of CDSConv by first initializing the Softmax temperature by 1. This hyperparameter was then decreased over training time until it reached 0.01 to produce the sparse output for scale selection. The entire network CDS-MVSNet was trained with 30 epochs, using the SGD optimizer with an initial learning rate of 0.01, and on two NVIDIA Titan V GPU with a batch size of 6. |