ContextGS : Compact 3D Gaussian Splatting with Anchor Level Context Model
Authors: Yufei Wang, Zhihao Li, Lanqing Guo, Wenhan Yang, Alex Kot, Bihan Wen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the models on several real-scene datasets, including Bungee Ne RF [32], Deep Blending [14], Mip-Ne RF360 [3], and Tanks&Temples [16]. To more comprehensively evaluate the performance of our method, following the previous prototype [5], we use all 9 scenes in Mip-Ne RF360 [3]. The detailed results of each scene are reported in the Appendix A.3. To further evaluate the performance models among a wide range of compression ratios, we use Rate-Dsitoration (RD) curves as an additional metric. [...] 5.3 Ablation studies and discussions |
| Researcher Affiliation | Academia | Yufei Wang1 Zhihao Li1 Lanqing Guo1 Wenhan Yang2 Alex C. Kot1 Bihan Wen1 1 Nanyang Technological University, Singapore 2 Peng Cheng Laboratory, China {yufei001, zhihao.li, lanqing.guo, eackot, bihan.wen}@ntu.edu.sg yangwh@pcl.ac.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Homepage: https://github.com/wyf0912/Context GS [...] We will release the code and pretrained models on acceptance. |
| Open Datasets | Yes | We evaluate the performance of the models on several real-scene datasets, including Bungee Ne RF [32], Deep Blending [14], Mip-Ne RF360 [3], and Tanks&Temples [16]. |
| Dataset Splits | No | The paper mentions 'training iterations' and 'training loss' but does not provide specific details on how the dataset was split into training, validation, and test sets with percentages or counts. |
| Hardware Specification | Yes | All our experiments are done using a server with RTX3090s. |
| Software Dependencies | No | The paper mentions 'efficient CUDA implementation' but does not specify other software dependencies or their version numbers. |
| Experiment Setup | Yes | We build our method based on Scaffold-GS [20]. The number of levels is set to 3 for all experiments and the target ratio among two adjacent iterations is 0.2. hc is set to 4, i.e., the dimension of the hyper-prior feature is a fourth of the anchor feature dimension. For a fair comparison, the dimension of the anchor feature f is set to 50 following [5] and we set the same λm = 5e 4. The setting of λe is discussed in Appendix A.3 since different values are used to evaluate different rate-distortion tradeoffs. For a fair comparison, we use the same training iterations with Scaffold-GS [20] and HAC [5], i.e., 30000 iterations. Besides, we use the same hyperparameters for anchor growing as Scaffold-GS [20] so that the final model has a similar or even smaller number of anchors, leading to faster rendering speed. |