# Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey

arXiv: https://arxiv.org/abs/2507.14501 · HTML: https://mlsanity.com/paper/2507.14501/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 278 of its 307 references that are in the map.
- First posted on arXiv 19 Jul 2025.

## Builds on (most cited first, 30 of 278)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)

## Built on by (most cited first, 27 of 27)

- [TTT3R: 3D Reconstruction as Test-Time Training](https://mlsanity.com/paper/2509.26645/) (2025)
- [PAGE-4D: Disentangled pose and geometry estimation for vggt-4d perception](https://mlsanity.com/paper/2510.17568/) (2025)
- [Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective](https://mlsanity.com/paper/2604.14025/) (2026)
- [S2-MLLM: Boosting Spatial Reasoning Capability of MLLMs for 3D Visual Grounding with Structural Guidance](https://mlsanity.com/paper/2512.01223/) (2025)
- [Sat3DGen: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image](https://mlsanity.com/paper/2605.14984/) (2026)
- [Speeding Up the Learning of 3D Gaussians with Much Shorter Gaussian Lists](https://mlsanity.com/paper/2603.09277/) (2026)
- [PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space](https://mlsanity.com/paper/2607.05373/) (2026)
- [TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction](https://mlsanity.com/paper/2605.26115/) (2026)
- [MuSASplat: Efficient Sparse-View 3D Gaussian Splats via Lightweight Multi-Scale Adaptation](https://mlsanity.com/paper/2512.07165/) (2025)
- [OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder](https://mlsanity.com/paper/2603.16099/) (2026)
- [VersaQ-3D: Architecture Support for Visual Geometry Grounded Transformers via Versatile Quantization](https://mlsanity.com/paper/2601.20317/) (2026)
- [KitchenTwin: Semantically and Geometrically Grounded 3D Kitchen Digital Twins](https://mlsanity.com/paper/2603.24684/) (2026)
- [Uncertainty Quality of VGGT: An Analysis on the DTU Benchmark Dataset](https://mlsanity.com/paper/2606.16479/) (2026)
- [Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration](https://mlsanity.com/paper/2607.16352/) (2026)
- [Think Locally, Refine Globally for Memory-Efficient 3D Reconstruction](https://mlsanity.com/paper/2609.21437/) (2026)
- [Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction](https://mlsanity.com/paper/2609.39960/) (2026)
- [Room Envelopes: A Synthetic Dataset for Indoor Layout Reconstruction from Images](https://mlsanity.com/paper/2511.03970/) (2025)
- [Make-It-Poseable: Feed-forward Latent Posing Model for 3D Characters](https://mlsanity.com/paper/2512.16767/) (2025)
- [ReCoSplat: Online Feed-Forward Gaussian Splatting via Render-and-Compare](https://mlsanity.com/paper/2603.09968/) (2026)
- [DriveFix: Spatio-Temporally Coherent Driving Scene Restoration](https://mlsanity.com/paper/2603.16306/) (2026)
- [AdvSplat: Adversarial Attacks on Feed-Forward Gaussian Splatting Models](https://mlsanity.com/paper/2603.23686/) (2026)
- [FurnSet: Exploiting Repeats for 3D Scene Reconstruction](https://mlsanity.com/paper/2604.20093/) (2026)
- [EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction](https://mlsanity.com/paper/2605.28101/) (2026)
- [Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis](https://mlsanity.com/paper/2606.01590/) (2026)
- [CasDeblurGS: Cascaded 2D-to-3D Multi-View Consistency for 3D Gaussian Splatting from Two Blurry Images](https://mlsanity.com/paper/2608.10345/) (2026)
- [LightBridge: Feed-Forward Generative Relighting for 3D Gaussian Splatting](https://mlsanity.com/paper/2609.02543/) (2026)
- [CoTL: Compact Token Learning for 3D Scene Representation](https://mlsanity.com/paper/2610.18446/) (2026)

## Abstract (arXiv)

3D reconstruction and view synthesis are foundational problems in computer vision, graphics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and digital twins. Traditional methods rely on computationally intensive iterative optimization in a complex chain, limiting their applicability in real-world scenarios. Recent advances in feed-forward approaches, driven by deep learning, have revolutionized this field by enabling fast and generalizable 3D reconstruction and view synthesis. This survey offers a comprehensive review of feed-forward techniques for 3D reconstruction and view synthesis, with a taxonomy according to the underlying representation architectures including point cloud, 3D Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), etc. We examine key tasks such as pose-free reconstruction, dynamic 3D reconstruction, and 3D-aware image and video synthesis, highlighting their applications in digital humans, SLAM, robotics, and beyond. In addition, we review commonly used datasets with detailed statistics, along with evaluation protocols for various downstream tasks. We conclude by discussing open research challenges and promising directions for future work, emphasizing the potential of feed-forward approaches to advance the state of the art in 3D vision.