Fast and Lightweight Novel View Synthesis with Differentiable Multiplane Image

• URL: http://arxiv.org/abs/2606.02068v1
• Date: Mon, 01 Jun 2026 10:57:53 GMT
• Title: Fast and Lightweight Novel View Synthesis with Differentiable Multiplane Image
• Abstract summary: We introduce one-step diffusion, which participates in both the differentiable optimization of MPI and the postprocessing of rendering results.<n>Compared with a representative GS-based method, our approach is 30.7% faster and uses only 14.8% of its model size.
• Score: 0.16710828103064077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, novel view synthesis has witnessed remarkable progress, with mainstream methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) delivering impressive results. However, these approaches often struggle to balance rendering speed and model size, and their optimization-based training can be highly time-consuming. Furthermore, they typically rely on dense observations, often failing to produce satisfactory results under sparse-view conditions. Although feed-forward reconstruction significantly reduces the optimization time of 3DGS, its pixel-aligned formulation generates millions of Gaussians from a single image, severely limiting its practical deployment on mobile devices. To address these limitations, we revisit the Multiplane Image(MPI) representation, which represents scenes using a compact set of planar layers for efficient novel view synthesis. Leveraging recent advances in visual foundation models, we utilize predicted point maps for reliable geometric initialization, followed by differentiable optimization. To address the issues of holes and artifacts in sparsely initialized MPI, we introduce one-step diffusion, which participates in both the differentiable optimization of MPI and the postprocessing of rendering results. Compared with a representative GS-based method, our approach is 30.7% faster and uses only 14.8% of its model size, while achieving competitive synthesis quality on front-view scenarios

Related papers

• Compact Neural Appearance Models for Efficient Gaussian Splatting [1.579851447355843]
  primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearance using low-order spherical harmonics (SH)<n>We present a thorough, end-to-end comparison of SH and recent spherical appearance models.<n>We introduce an implicit alternative that decodes compact per-primitive latent codes using a tiny shared pipeline.
  arXiv  Detail & Related papers  (2026-09-04T15:18:54Z)
• Gaussian On-the-Fly Splatting: A Progressive Framework for Robust Near Real-Time 3DGS Optimization [8.422116335889163]
  3D Gaussian Splatting (3DGS) achieves high-fidelity rendering with fast real-time performance.<n>Existing methods rely on offline training after full Structure-from-Motion (SfM) processing.<n>We introduce On-the-Fly GS, a progressive framework enabling near real-time 3DGS optimization during image capture.
  arXiv  Detail & Related papers  (2025-03-17T11:47:58Z)
• MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [100.90743697473232]
  Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering.<n>Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images.<n>We propose a view synthesis framework based on 3D Gaussian Splatting, enabling scene reconstruction from sparse views.
  arXiv  Detail & Related papers  (2024-10-15T08:39:05Z)
• MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo [54.00987996368157]
  We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) MVSGaussian achieves real-time rendering with better synthesis quality for each scene.
  arXiv  Detail & Related papers  (2024-05-20T17:59:30Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)
• CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians [18.42203035154126]
  We introduce a structured Gaussian representation that can be controlled in 2D image space.<n>We then constraint the Gaussians, in particular their position, and prevent them from moving independently during optimization.<n>We demonstrate significant improvements compared to the state-of-the-art sparse-view NeRF-based approaches on a variety of scenes.
  arXiv  Detail & Related papers  (2024-03-28T15:27:13Z)
• FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting [58.41056963451056]
  We propose a few-shot view synthesis framework based on 3D Gaussian Splatting. This framework enables real-time and photo-realistic view synthesis with as few as three training views. FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets.
  arXiv  Detail & Related papers  (2023-12-01T09:30:02Z)
• GS-IR: 3D Gaussian Splatting for Inverse Rendering [71.14234327414086]
  We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) We extend GS, a top-performance representation for novel view synthesis, to estimate scene geometry, surface material, and environment illumination from multi-view images captured under unknown lighting conditions. The flexible and expressive GS representation allows us to achieve fast and compact geometry reconstruction, photorealistic novel view synthesis, and effective physically-based rendering.
  arXiv  Detail & Related papers  (2023-11-26T02:35:09Z)
• Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day [63.96075838322437]
  We propose a framework to learn a single-image novel-view synthesizer. Our framework is able to reduce the total training time from 10 days to less than 1 day.
  arXiv  Detail & Related papers  (2023-10-04T17:57:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.