title: ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
summary: 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.
paper_authors._id: 683919ddf85de1fc563e0d1c
paper_authors.name: Weijie Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:53:41.133Z
paper_authors._id: 66699aa8a33847217b5a49c7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png
paper_authors.fullname: Weijie Wang
paper_authors.type: user
paper_authors.user: lhmd
paper_authors._id: 683919ddf85de1fc563e0d1d
paper_authors.name: Donny Y. Chen
paper_authors._id: 683919ddf85de1fc563e0d1e
paper_authors.name: Zeyu Zhang
paper_authors._id: 683919ddf85de1fc563e0d1f
paper_authors.name: Duochao Shi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:42:15.066Z
paper_authors._id: 661d1f83ea3df2195a7c2924
paper_authors.avatarUrl: /avatars/dec49fc1d79913b07b57ccbef079198f.svg
paper_authors.fullname: dcshi
paper_authors.type: user
paper_authors.user: dc-walker
paper_authors._id: 683919ddf85de1fc563e0d20
paper_authors.name: Akide Liu
paper_authors._id: 683919ddf85de1fc563e0d21
paper_authors.name: Bohan Zhuang
paper_title: ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
paper_summary: 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.
paper_ai_summary: ZPressor, a lightweight module, compresses multi-view inputs for feed-forward 3D Gaussian Splatting models, enhancing their scalability and performance under dense view settings.
paper_ai_keywords: Gaussian Splatting Information Bottleneck cross attention latent state multi-view inputs feed-forward models novel view synthesis DL3DV-10K RealEstate10K