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

• URL: http://arxiv.org/abs/2507.14501v2
• Date: Wed, 30 Jul 2025 03:32:37 GMT
• Title: Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey
• Authors: Jiahui Zhang, Yuelei Li, Anpei Chen, Muyu Xu, Kunhao Liu, Jianyuan Wang, Xiao-Xiao Long, Hanxue Liang, Zexiang Xu, Hao Su, Christian Theobalt, Christian Rupprecht, Andrea Vedaldi, Hanspeter Pfister, Shijian Lu, Fangneng Zhan,
• Abstract summary: 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.<n>Recent advances in feed-forward approaches, driven by deep learning, have revolutionized this field by enabling fast and generalizable 3D reconstruction and view synthesis.
• Score: 154.50661618628433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision [9.961452710097685]
  3D Low-Level Vision (3D LLV) extends classical 2D Low-Level Vision tasks into the 3D spatial domain.<n>Recent methods that integrate LLV into neural rendering frameworks are categorized to illustrate how they enable high-fidelity 3D reconstruction under adverse conditions.<n>This work positions 3D LLV as a fundamental direction for robust 3D content generation and scene-level reconstruction in real-world environments.
  arXiv  Detail & Related papers  (2025-06-19T12:25:46Z)
• DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos [52.46386528202226]
  We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM)<n>It is the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene.<n>It achieves performance on par with state-of-the-art monocular video 3D tracking methods.
  arXiv  Detail & Related papers  (2025-06-11T17:59:58Z)
• A Survey of 3D Reconstruction with Event Cameras [16.103940503726022]
  Event cameras produce sparse yet temporally dense data streams, enabling robust and accurate 3D reconstruction.<n>These capabilities offer substantial promise for transformative applications across various fields, including autonomous driving, robotics, aerial navigation, and immersive virtual reality.<n>This survey aims to serve as an essential reference and provides a clear and motivating roadmap toward advancing the state of the art in event-driven 3D reconstruction.
  arXiv  Detail & Related papers  (2025-05-13T11:04:04Z)
• 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)
• A Generative Approach to High Fidelity 3D Reconstruction from Text Data [0.0]
  This research proposes a fully automated pipeline that seamlessly integrates text-to-image generation, various image processing techniques, and deep learning methods for reflection removal and 3D reconstruction.<n>By leveraging state-of-the-art generative models like Stable Diffusion, the methodology translates natural language inputs into detailed 3D models through a multi-stage workflow.<n>This approach addresses key challenges in generative reconstruction, such as maintaining semantic coherence, managing geometric complexity, and preserving detailed visual information.
  arXiv  Detail & Related papers  (2025-03-05T16:54:15Z)
• Survey on Fundamental Deep Learning 3D Reconstruction Techniques [0.0]
  This survey aims to investigate fundamental deep learning (DL) based 3D reconstruction techniques that produce photo-realistic 3D models and scenes. We dissect the underlying algorithms, evaluate their strengths and tradeoffs, and project future research trajectories in this rapidly evolving field.
  arXiv  Detail & Related papers  (2024-07-11T02:30:05Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review [0.08823202672546056]
  This review paper focuses on state-of-the-art techniques for 3D reconstruction, including the generation of novel, unseen views. An overview of recent developments in the Gaussian Splatting method is provided, covering input types, model structures, output representations, and training strategies.
  arXiv  Detail & Related papers  (2024-05-06T12:32:38Z)
• Recent Trends in 3D Reconstruction of General Non-Rigid Scenes [104.07781871008186]
  Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthesizing of photorealistic novel views, useful for the movie industry and AR/VR applications. This state-of-the-art report (STAR) offers the reader a comprehensive summary of state-of-the-art techniques with monocular and multi-view inputs.
  arXiv  Detail & Related papers  (2024-03-22T09:46:11Z)
• A Survey on 3D Gaussian Splatting [51.96747208581275]
  3D Gaussian splatting (GS) has emerged as a transformative technique in explicit radiance field and computer graphics.<n>We provide the first systematic overview of the recent developments and critical contributions in the domain of 3D GS.<n>By enabling unprecedented rendering speed, 3D GS opens up a plethora of applications, ranging from virtual reality to interactive media and beyond.
  arXiv  Detail & Related papers  (2024-01-08T13:42:59Z)

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.