InvSplat: Inverse Feed-Forward Scene Splatting

• URL: http://arxiv.org/abs/2607.02301v1
• Date: Thu, 02 Jul 2026 15:18:08 GMT
• Title: InvSplat: Inverse Feed-Forward Scene Splatting
• Abstract summary: Inverse rendering aims to recover both 3D geometry and physically meaningful material properties from images.<n>We present a feed-forward multi-view reconstruction framework for inverse rendering.<n>Our model integrates priors from a material estimation network with a multi-view 3D reconstruction backbone.
• Score: 27.461734130974097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inverse rendering aims to recover both 3D geometry and physically meaningful material properties from images, enabling applications such as relighting and novel view synthesis. Optimization-based methods achieve high fidelity but require costly per-scene fitting, while image-space learning-based approaches often suffer from multi-view inconsistencies and lack an explicit 3D representation for stable novel view rendering. We present a feed-forward multi-view reconstruction framework for inverse rendering that directly predicts a structured 3D Gaussian representation with intrinsic material attributes. Each Gaussian primitive is parameterized by mean, normal, opacity, rotation, scale, albedo, metallic, and roughness, enabling a disentangled and physically grounded scene representation. Our model integrates priors from a material estimation network with a multi-view 3D reconstruction backbone, allowing joint prediction of geometry and reflectance parameters in a single forward pass. Experiments on synthetic and real-world datasets demonstrate improved multi-view consistency compared to 2D baselines, accurate material recovery, and stable novel view rendering. Our representation further supports physically-based relighting and more faithful modeling of view-dependent effects compared to existing RGB-based feed-forward reconstruction methods. Our project webpage is: $\href{https://poliik.github.io/invsplat/}{\text{https://poliik.github.io/invsplat/}}$.

Related papers

• TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction [65.03268438396556]
  TriSplat is a feed-forward reconstruction network that represents scenes with oriented triangle primitives.<n>It exports simulation-ready mesh scenes from a single forward pass.<n>Experiments on RealEstate10K and DL3DV show that this representation produces more geometry-faithful reconstructions than Gaussian feed-forward baselines.
  arXiv  Detail & Related papers  (2026-05-25T17:59:53Z)
• HGGT: Robust and Flexible 3D Hand Mesh Reconstruction from Uncalibrated Images [81.42866295265443]
  High-fidelity 3D hand geometry is a critical task in computer vision.<n> scalable applications demand both accuracy and deployment flexibility.<n>We propose a feed-forward architecture that infers 3D hand meshes and camera poses from uncalibrated views.
  arXiv  Detail & Related papers  (2026-03-25T06:54:34Z)
• ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination [44.640886020982464]
  ReLi3D is the first unified end-to-end pipeline that simultaneously reconstructs complete 3D geometry, spatially-varying physically-based materials, and environment illumination from multi-view images in under one second.<n>Key to our approach is the fusion of the multi-view input via a transformer cross-conditioning architecture, followed by a novel unified two-path prediction strategy.
  arXiv  Detail & Related papers  (2026-03-20T08:37:39Z)
• SplatBright: Generalizable Low-Light Scene Reconstruction from Sparse Views via Physically-Guided Gaussian Enhancement [26.905118897488077]
  SplatBright is the first generalizable 3D Gaussian framework for joint low-light enhancement and reconstruction from sparse sRGB inputs.<n>Our key idea is to integrate physically guided illumination modeling with geometry-appearance decoupling for consistent low-light reconstruction.<n>Experiments on public and self-collected datasets demonstrate that SplatBright achieves superior novel view synthesis, cross-view consistency, and better generalization to unseen low-light scenes compared with both 2D and 3D methods.
  arXiv  Detail & Related papers  (2025-12-21T09:06:16Z)
• LARM: A Large Articulated-Object Reconstruction Model [29.66486888001511]
  LARM is a unified feedforward framework that reconstructs 3D articulated objects from sparse-view images.<n>LARM generates auxiliary outputs such as depth maps and part masks to facilitate explicit 3D mesh extraction and joint estimation.<n>Our pipeline eliminates the need for dense supervision and supports high-fidelity reconstruction across diverse object categories.
  arXiv  Detail & Related papers  (2025-11-14T18:55:27Z)
• LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields [23.174562444342286]
  We present Large Inverse Rendering Model (LIRM), a transformer architecture that jointly reconstructs high-quality shape, materials, and radiance fields.<n>Our model builds upon the recent Large Reconstruction Models (LRMs) that achieve state-of-the-art sparse-view reconstruction quality.
  arXiv  Detail & Related papers  (2025-04-28T17:48:58Z)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• 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)
• VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction [64.09702079593372]
  VolRecon is a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF) On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction.
  arXiv  Detail & Related papers  (2022-12-15T18:59:54Z)
• High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
  We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
  arXiv  Detail & Related papers  (2022-11-28T18:59:52Z)
• Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
  We present an efficient method for joint optimization of materials and lighting from multi-view image observations. We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
  arXiv  Detail & Related papers  (2021-11-24T13:58:20Z)
• Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing [16.975014467319443]
  Differentiable path tracing is an appealing framework as it can reproduce complex appearance effects. We show how to use differentiable ray tracing to refine an initial coarse mesh and per-mesh-facet material representation. We also show how to refine initial reconstructions of real-world objects in unconstrained environments.
  arXiv  Detail & Related papers  (2020-12-06T18:55:35Z)

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.