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..
VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
Authors: Hanlin Chen, Fangyin Wei, Chen Li, Tianxin Huang, Yunsong Wang, Gim Hee Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared with Gaussian-based baselines, experiments show that our approach obtains better reconstruction quality and maintains competitive appearance quality at faster training speed and 100+ FPS rendering. |
| Researcher Affiliation | Academia | 1 School of Computing, National University of Singapore 2 Princeton University EMAIL EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] Justification: Our codes have been released. (NeurIPS Paper Checklist, Q5). The project website linked in the paper (https://hlinchen.github.io/projects/VCR-GauS/) states "Code & Data (Coming soon)". |
| Open Datasets | Yes | For surface reconstruction, we evaluate on Tanks and Temples (TNT) [24]. To further validate the effectiveness of our method, we compare with other methods on Replica [43]. Although we focus on the large-scale reconstruction, we also report our results on DTU [21], which can be seen in the supplementary. Furthermore, we evaluate the rendering results on Mip-Ne RF360 [3]. |
| Dataset Splits | Yes | We use the same train and test data with 2DGS on TNT, Mip-Ne RF360, and DTU datasets. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA 3090/4090/A5000/A6000 GPU. |
| Software Dependencies | Yes | We use PyTorch 2.0.1 and CUDA 11.8. |
| Experiment Setup | Yes | λ1, λ2, and λ3 are set to 1, 0.01, and 0.015, respectively. The densification threshold β is set to 0.002. The hyperparameter γ is set to 0.005. We use pretrained DSINE [1] to predict normal maps for outdoor scenes and pretrained Geo Wizard [13] for indoor scenes. Similar to 3DGS, we stop densification at 15k iterations and optimize all of our model parameters for 30k iterations. |