# VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with Voxel-Aligned Prediction

arXiv: https://arxiv.org/abs/2509.19297 · HTML: https://mlsanity.com/paper/2509.19297/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 70 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 30 of 50)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction](https://mlsanity.com/paper/2312.12337/) (2023)
- [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023)
- [MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images](https://mlsanity.com/paper/2403.14627/) (2024)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.19702/) (2024)
- [OctNet: Learning Deep 3D Representations at High Resolutions](https://mlsanity.com/paper/1611.05009/) (2016)
- [DepthSplat: Connecting Gaussian Splatting and Depth](https://mlsanity.com/paper/2410.13862/) (2024)
- [FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting](https://mlsanity.com/paper/2312.00451/) (2023)
- [Unifying Flow, Stereo and Depth Estimation](https://mlsanity.com/paper/2211.05783/) (2022)
- [No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images](https://mlsanity.com/paper/2410.24207/) (2024)
- [Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image](https://mlsanity.com/paper/2012.09855/) (2020)
- [AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views](https://mlsanity.com/paper/2505.23716/) (2025)
- [Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians](https://mlsanity.com/paper/2403.17898/) (2024)

## Built on by (most cited first, 30 of 48)

- [C3G: Learning Compact 3D Representations with 2K Gaussians](https://mlsanity.com/paper/2512.04021/) (2025)
- [Latent Spatial Memory for Video World Models](https://mlsanity.com/paper/2606.09828/) (2026)
- [DriveGen3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion](https://mlsanity.com/paper/2510.15264/) (2025)
- [TokenSplat: Token-aligned 3D Gaussian Splatting for Feed-forward Pose-free Reconstruction](https://mlsanity.com/paper/2603.00697/) (2026)
- [GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens](https://mlsanity.com/paper/2604.15284/) (2026)
- [Off The Grid: Detection of Primitives for Feed-Forward 3D Gaussian Splatting](https://mlsanity.com/paper/2512.15508/) (2025)
- [World-R1: Reinforcing 3D Constraints for Text-to-Video Generation](https://mlsanity.com/paper/2604.24764/) (2026)
- [AnchorSplat: Feed-Forward 3D Gaussian Splatting with 3D Geometric Priors](https://mlsanity.com/paper/2604.07053/) (2026)
- [SparseSplat: Towards Applicable Feed-Forward 3D Gaussian Splatting with Pixel-Unaligned Prediction](https://mlsanity.com/paper/2604.03069/) (2026)
- [Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective](https://mlsanity.com/paper/2604.14025/) (2026)
- [AnyDepth: Depth Estimation Made Easy](https://mlsanity.com/paper/2601.02760/) (2026)
- [Mix3R: Mixing Feed-forward Reconstruction and Generative 3D Priors for Joint Multi-view Aligned 3D Reconstruction and Pose Estimation](https://mlsanity.com/paper/2605.03359/) (2026)
- [EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis](https://mlsanity.com/paper/2601.15951/) (2026)
- [MotionWeaver: Holistic 4D-Anchored Framework for Multi-Humanoid Image Animation](https://mlsanity.com/paper/2602.13326/) (2026)
- [TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction](https://mlsanity.com/paper/2605.26115/) (2026)
- [Free-Range Gaussians: Non-Grid-Aligned Generative 3D Gaussian Reconstruction](https://mlsanity.com/paper/2604.04874/) (2026)
- [UniMesh: Unifying 3D Mesh Understanding and Generation](https://mlsanity.com/paper/2604.17472/) (2026)
- [SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis](https://mlsanity.com/paper/2605.07287/) (2026)
- [Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction](https://mlsanity.com/paper/2605.31595/) (2026)
- [GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds](https://mlsanity.com/paper/2604.20155/) (2026)
- [AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting](https://mlsanity.com/paper/2605.10239/) (2026)
- [Learn2Splat: Extending the Horizon of Learned 3DGS Optimization](https://mlsanity.com/paper/2605.15760/) (2026)
- [VG2GT: Voxel-Gaussian Splatting Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2606.01573/) (2026)
- [HiReFF: High-Resolution Feedforward Human Reconstruction from Uncalibrated Sparse-View Video](https://mlsanity.com/paper/2606.29333/) (2026)
- [Bridging 3D Gaussians and Semantic Occupancy for Comprehensive Open-Vocabulary Scene Understanding from Unposed Images](https://mlsanity.com/paper/2607.01633/) (2026)
- [ATSplat: Compact Feed-forward 3D Gaussian Splatting with Adaptive Token Expansion](https://mlsanity.com/paper/2607.20417/) (2026)
- [S2D: Sparse to Dense Lifting for 3D Reconstruction with Minimal Inputs](https://mlsanity.com/paper/2603.10893/) (2026)
- [Leveling3D: Leveling Up 3D Reconstruction with Feed-Forward 3D Gaussian Splatting and Geometry-Aware Generation](https://mlsanity.com/paper/2603.16211/) (2026)
- [GO-Renderer: Generative Object Rendering with 3D-aware Controllable Video Diffusion Models](https://mlsanity.com/paper/2603.23246/) (2026)
- [Fus3D: Decoding Consolidated 3D Geometry from Feed-forward Geometry Transformer Latents](https://mlsanity.com/paper/2603.25827/) (2026)

## Abstract (arXiv)

Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective solution for novel view synthesis. Existing methods predominantly rely on a pixel-aligned Gaussian prediction paradigm, where each 2D pixel is mapped to a 3D Gaussian. We rethink this widely adopted formulation and identify several inherent limitations: it renders the reconstructed 3D models heavily dependent on the number of input views, leads to view-biased density distributions, and introduces alignment errors, particularly when source views contain occlusions or low texture. To address these challenges, we introduce VolSplat, a new multi-view feed-forward paradigm that replaces pixel alignment with voxel-aligned Gaussians. By directly predicting Gaussians from a predicted 3D voxel grid, it overcomes pixel alignment's reliance on error-prone 2D feature matching, ensuring robust multi-view consistency. Furthermore, it enables adaptive control over density based on 3D scene complexity, yielding more faithful Gaussians, improved geometric consistency, and enhanced novel-view rendering quality. Experiments on widely used benchmarks demonstrate that VolSplat achieves state-of-the-art performance, while producing more plausible and view-consistent results. The video results, code and trained models are available on our project page: https://lhmd.top/volsplat.