{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/consistent-quantity-quality-control-across","title":"Consistent Quantity-Quality Control across Scenes for Deployment-Aware Gaussian Splatting","arxiv_id":"2505.10473","date":"2025-05-15","proceeding":null,"authors":["Fengdi Zhang","Hongkun Cao","Ruqi Huang"],"abstract":"To reduce storage and computational costs, 3D Gaussian splatting (3DGS) seeks to minimize the number of Gaussians used while preserving high rendering quality, introducing an inherent trade-off between Gaussian quantity and rendering quality. Existing methods strive for better quantity-quality performance, but lack the ability for users to intuitively adjust this trade-off to suit practical needs such as model deployment under diverse hardware and communication constraints. Here, we present ControlGS, a 3DGS optimization method that achieves semantically meaningful and cross-scene consistent quantity-quality control while maintaining strong quantity-quality performance. Through a single training run using a fixed setup and a user-specified hyperparameter reflecting quantity-quality preference, ControlGS can automatically find desirable quantity-quality trade-off points across diverse scenes, from compact objects to large outdoor scenes. It also outperforms baselines by achieving higher rendering quality with fewer Gaussians, and supports a broad adjustment range with stepless control over the trade-off.","url_abs":"https://arxiv.org/abs/2505.10473v1","url_pdf":"https://arxiv.org/pdf/2505.10473v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"consistent-quantity-quality-control-across","repo_url":"https://github.com/zhang-fengdi/ControlGS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3dgs","task_name":"3DGS"},{"task_slug":"model-compression","task_name":"Model Compression"},{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}