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 [1].
Enhancing Close-up Novel View Synthesis via Pseudo-labeling
Authors: Jiatong Xia, Libo Sun, Lingqiao Liu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate the efficacy of our approach. |
| Researcher Affiliation | Academia | Australian Institute for Machine Learning, The University of Adelaide EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and prose, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/Jiatong Xia/Close-up-View Pseudo-Labeling.git |
| Open Datasets | No | Furthermore, we have developed a dataset specifically designed to evaluate the generation of close-up views. This dataset addresses the current lack of benchmarks for assessing the performance of existing and future methods in this domain. |
| Dataset Splits | Yes | Our dataset comprises diverse scenes, each one is extracted as a subset of frames from a captured video, with 50 to 100 training images and 10 to 20 testing images for each scene, images are captured at a resolution of 960 540. |
| Hardware Specification | Yes | The experiments are conducted on NVIDIA 3090 GPUs and the Adam optimizer (Kingma and Ba 2015) is employed to optimize the radiance field. |
| Software Dependencies | No | The paper mentions the Adam optimizer but does not specify any software libraries or frameworks with version numbers used for implementation. |
| Experiment Setup | Yes | After that, for Ne RF as baseline method, we load the weights of the pre-trained Ne RF model and optimized for 10K iterations per scene with a ray batch of original training samples and generated samples set to 2048 (1024 for each). For 2DGS as baseline method, we optimized the pre-trained 2DGS model for 500 iterations per scene with each iteration combine supervision from both training image and masked pseudo label. For test-time finetuning, we applied our method on both Ne RF and 2DGS. Specifically, we optimized the pre-trained 2DGS model with original training samples and generated samples together for iterations of 5 times for each test view. And for Ne RF, we optimized the pre-trained model for 200 iterations on test poses, with a batch size of 1024 for each samples. We randomly select the value of λ in Eq. 8 from the range of (2, 8) for each iteration. And set the value of ε in Eq. 10 to π 4. ϵ denotes the threshold for determining whether two RGB values can be considered as matched, and we set ϵ to 0.05 in this paper. |