用于高效基于高斯的图像表示的局部感知密度控制
Locality-Aware Density Control for Efficient Gaussian-based Image Representation
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中文总结 AI 辅助
研究二维高斯喷溅图像表示中高斯容量分配低效问题，提出局部感知密度控制框架LocoADC，通过区域高斯致密化和相似性驱动高斯合并策略及局部颜色一致性约束，有效提升高斯容量利用率，改进图像表示。
中文摘要 AI 辅助
二维高斯喷溅因其明确的公式、快速的光栅化和良好的解码效率，是图像表示的一个有吸引力的方向。该范式的表示质量取决于高斯容量在需求区域的适当分配。现有方法在优化过程中未能有效分配高斯容量。我们提出了局部感知密度控制（LocoADC），一个即插即用的框架，通过区域高斯致密化（RGD）和相似性驱动高斯合并（SDGM）策略提高高斯容量利用率，还有局部颜色一致性约束实现更可靠的合并。实验表明LocoADC通过更有效的局部高斯分配持续改进多个基线。
英文摘要
2D Gaussian Splatting is an attractive direction for image representation due to its explicit formulation, fast rasterization, and favorable decoding efficiency. The representation quality of this paradigm depends on the proper allocation of Gaussian capacity to the demanding regions. However, existing methods fail to allocate Gaussian capacity efficiently during optimization: under-reconstructed content is often refined in a fragmented pixel-wise manner, while neighboring optimized Gaussians with similar attributes are redundantly retained. This inefficiency motivates the need for a density control framework that jointly addresses insufficient allocation in under-reconstructed regions and redundant allocation in over-reconstructed regions. Our key insight is that this framework should exploit two complementary forms of locality: the local continuity of reconstruction errors in image space for improved Gaussian allocation, and the local similarity of neighboring Gaussians in Gaussian space for redundant elimination. Based on this insight, we propose Locality-Aware Density Control (LocoADC), a plug-and-play framework that improves Gaussian capacity utilization through Region-wise Gaussian Densification (RGD) and Similarity-Driven Gaussian Merging (SDGM) strategies, together with a local color consistency constraint for more reliable merging. Extensive experiments on diverse datasets show that LocoADC consistently improves multiple baselines by enabling more effective local Gaussian allocation, including a 2.93 dB PSNR gain over GI on the CLIC dataset under the same 30k Gaussian budget. Code is available at: \textit{https://github.com/ChenJiaCong-1005/LocoADC}.
发表机构
- Shenzhen University（深圳大学）
- Pengcheng Laboratory（鹏城实验室）
- Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。
