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..
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 | Venue PDF | 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. |