Inner-Outer Aware Reconstruction Model for Monocular 3D Scene Reconstruction
Authors: Yu-Kun Qiu, Guo-Hao Xu, Wei-Shi Zheng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on Scan Net, ICL-NUIM and TUM-RGBD datasets demonstrate the effectiveness and generalization of our model. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Sun Yat-sen University, China 2 Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China |
| Pseudocode | No | The paper includes Figure 2 which illustrates the overall pipeline, but this is a diagram and not a structured pseudocode block or algorithm. |
| Open Source Code | Yes | The code is available at https: //github.com/York Qiu/Inner Outer Aware Reconstruction. |
| Open Datasets | Yes | Following previous works [8, 9, 11], we trained models on the Scan Net [40] dataset. ... Previous works evaluate the generalization performance of the models trained on Scan Net on TUM-RGBD [41] and ICL-NUIM [42] datasets. |
| Dataset Splits | Yes | We follow the official train/eval/test split, where 1201 videos are used for training, 312 videos are used for evaluating and 100 videos are used for testing. |
| Hardware Specification | Yes | Training our model takes about 90 hours on a single Nvidia RTX 3090 graphic card. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer [43]', 'Mnas Net-B1 [44]', and 'feature pyramid network [45]' but does not provide specific version numbers for these or other software libraries (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | We use the Adam optimizer [43] with β1 = 0.9, β2 = 0.999 and ϵ = 10 8. The learning rate is set to α = 10 3 and is linearly warmed up from 10 10 over 2000 steps. We trained our model for 500 epochs. ... The CNN backbone is fixed in the first 350 epochs and is finetuned with a learning rate α = 10 4 in the last 150 epochs. The batch size is set to 4 and drops to 2 in the finetuning stage. ... the voxel size of the fine/medium/coarse level is set to 4cm/8cm/16cm and the TSDF truncation distance is set to triple the voxel size. |