Neural Field Classifiers via Target Encoding and Classification Loss
Authors: Xindi Yang, Zeke Xie, Xiong Zhou, Boyu Liu, Buhua Liu, Yi Liu, Haoran Wang, YUNFENG CAI, Mingming Sun
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the impressive effectiveness of NFC at the nearly free extra computational costs. Moreover, NFC also shows robustness to sparse inputs, corrupted images, and dynamic scenes. |
| Researcher Affiliation | Collaboration | µBeijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University γCognitive Computing Lab, Baidu Research |
| Pseudocode | No | The paper describes the methodology using text and diagrams (Figure 1 and 2), but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/Madaoer/Neural-Field-Classifier. |
| Open Datasets | Yes | We choose the Replica Dataset and Tanks and Temples Advanced(T & T) as the benchmark datasets. The Replica Dataset encompasses eight complex static scenes... T & T Dataset is a popular 3D reconstruction dataset... We also use the classical Chair scene for evaluating NFC and NFR with Strivec (Gao et al., 2023). |
| Dataset Splits | Yes | In the experiments on Replica, if one image index is divisible by 10, we move the image to the test dataset; if not, we move the image to the training dataset. Thus, we have 90% images for training and 10% images for evaluation. |
| Hardware Specification | Yes | Table 7: Ablation study I. Model: DVGO. Dataset: Replica Scene 6. GPU: A100. Table 8: Ablation study II. Model: Neu S. Dataset: Replica Scene 7. GPU: A100. |
| Software Dependencies | No | The paper mentions software like 'Adam' (an optimizer), 'Nerf-pytorch' (implied from citation), and 'SDFStudio' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | DVGO Setting We employ the sourcecode of DVGO (Version 2) in the original project (Sun et al., 2022) without modifying training hyperparameters. So we train DVGO via Adam (Kingma and Ba, 2015) with the batch size B = 4096. The learning rate of density voxel grids and color/feature voxel grids is 0.1, and the learning rate of the RGB net (MLP) is 0.001. The total number of iterations is 30000. We multiply the learning rate by 0.1 per 1000 iterations. |