ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
Authors: name: Weijie Wang, name: Donny Y. Chen, name: Zeyu Zhang, name: Duochao Shi, name: Akide Liu, name: Bohan Zhuang
Organization: ZIP Lab

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.