ZPressor：面向可扩展前馈3D高斯散射的瓶颈感知压缩技术

ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS

May 29, 2025

作者: Weijie Wang, Donny Y. Chen, Zeyu Zhang, Duochao Shi, Akide Liu, Bohan Zhuang

cs.AI

摘要

前馈式3D高斯溅射（3DGS）模型近期作为新视角合成的一种有前景解决方案崭露头角，它支持单次推理而无需针对每个场景进行3DGS优化。然而，其可扩展性从根本上受到编码器容量有限的制约，随着输入视图数量的增加，性能会下降或内存消耗会激增。在本研究中，我们通过信息瓶颈原理的视角分析了前馈式3DGS框架，并引入了ZPressor——一个轻量级、架构无关的模块，它能够高效地将多视图输入压缩为一个紧凑的潜在状态Z，该状态保留了场景的关键信息同时剔除了冗余。具体而言，ZPressor通过将视图划分为锚点集和支持集，并利用交叉注意力机制将支持视图的信息压缩至锚点视图中，形成压缩后的潜在状态Z，使得现有的前馈式3DGS模型能够在80GB GPU上处理超过100个480P分辨率的输入视图。我们展示了将ZPressor集成到多个先进的前馈式3DGS模型中，在中等数量输入视图下持续提升性能，并在DL3DV-10K和RealEstate10K两大基准测试的密集视图设置下增强了鲁棒性。视频结果、代码及训练模型可在我们的项目页面获取：https://lhmd.top/zpressor。

English

Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene 3DGS optimization. However, their scalability is fundamentally constrained by the limited capacity of their encoders, leading to degraded performance or excessive memory consumption as the number of input views increases. In this work, we analyze feed-forward 3DGS frameworks through the lens of the Information Bottleneck principle and introduce ZPressor, a lightweight architecture-agnostic module that enables efficient compression of multi-view inputs into a compact latent state Z that retains essential scene information while discarding redundancy. Concretely, ZPressor enables existing feed-forward 3DGS models to scale to over 100 input views at 480P resolution on an 80GB GPU, by partitioning the views into anchor and support sets and using cross attention to compress the information from the support views into anchor views, forming the compressed latent state Z. We show that integrating ZPressor into several state-of-the-art feed-forward 3DGS models consistently improves performance under moderate input views and enhances robustness under dense view settings on two large-scale benchmarks DL3DV-10K and RealEstate10K. The video results, code and trained models are available on our project page: https://lhmd.top/zpressor.