NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
Authors: Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, Wenping Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the DTU dataset and the Blended MVS dataset show that Neu S outperforms the state-of-the-arts in high-quality surface reconstruction, especially for objects and scenes with complex structures and self-occlusion. |
| Researcher Affiliation | Academia | The University of Hong Kong Max Planck Institute for Informatics Texas A&M University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | To evaluate our approach and baseline methods, we use 15 scenes from the DTU dataset [11]... We further tested on 7 challenging scenes from the low-res set of the Blended MVS dataset [45](CC-4 License). |
| Dataset Splits | No | The paper mentions training models for a certain number of iterations but does not explicitly specify a validation dataset split or how validation was performed. |
| Hardware Specification | Yes | We sample 512 rays per batch and train our model for 300k iterations for 14 hours (for the w/ mask setting) and 16 hours (for the w/o mask setting) on a single NVIDIA RTX2080Ti GPU. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., Python, PyTorch, CUDA versions) used for the experiments. |
| Experiment Setup | Yes | We sample 512 rays per batch and train our model for 300k iterations for 14 hours (for the w/ mask setting) and 16 hours (for the w/o mask setting) on a single NVIDIA RTX2080Ti GPU. |