CRAYM: Neural Field Optimization via Camera RAY Matching
Authors: Liqiang Lin, Wenpeng Wu, Chi-Wing Fu, Hao Zhang, Hui Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on both the synthetic objects from Ne RF-Synthetic [26] and the real scenes from Urban Scene3D [22], for novel view synthesis and 3D geometry reconstruction, over denseand sparse-view settings. Compared to state-of-the-art alternatives, CRAYM produces superior results especially over fine details. |
| Researcher Affiliation | Academia | 1College of Computer Science and Software Engineering, Shenzhen University 2Department of Computer Science and Engineering, The Chinese University of Hong Kong 3School of Computing Science, Simon Fraser University linliqiang2020@gmail.com wenpengggg@gmail.com cwfu@cse.cuhk.edu.hk haoz@sfu.ca hhzhiyan@gmail.com |
| Pseudocode | No | No section or figure explicitly labeled "Pseudocode" or "Algorithm" was found. |
| 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: All the experiments use open access dataset. The code is not released yet. |
| Open Datasets | Yes | We evaluate our method on the Ne RF-Synthetic dataset [26] with eight synthetic objects (Section 4.2), the LLFF dataset [25], and the real scenes from the Urban Scene3D dataset [22] (Section 4.3). |
| Dataset Splits | Yes | Each image set contains 48 images. We use 90% of them as training set and the remaining 10% images as testing data. |
| Hardware Specification | Yes | All the experiments are conducted with an Nvidia GV100. |
| Software Dependencies | No | The paper mentions software like PyTorch and CUDA in the context of BARF [20] or similar methods and AdamW optimizer, but does not specify version numbers for these general software dependencies as part of the experimental setup. |
| Experiment Setup | Yes | The geometry network Φg is implemented as a three-layer MLP with the ReLU activation for the input and hidden layers. The texture network Φt is implemented as a four-layer MLP with the ReLU activation for the input and hidden layers. The whole network is optimized with the AdamW optimizer with a learning rate of 0.01, β = [0.9, 0.99], and ϵ = 1.0e-15. The variance [34] of the geometry network Φg is initialized as 0.3 and is optimized with a learning rate of 0.001. We adopt a warm-up training for the first 500 iterations with the Linear LR scheduler. |