Review of Feed-forward 3D Reconstruction: From DUSt3R to VGGT
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
3D reconstruction, which aims to recover the dense three-dimensional
structure of a scene, is a cornerstone technology for numerous applications,
including augmented/virtual reality, autonomous driving, and robotics. While
traditional pipelines like Structure from Motion (SfM) and Multi-View Stereo
(MVS) achieve high precision through iterative optimization, they are limited
by complex workflows, high computational cost, and poor robustness in
challenging scenarios like texture-less regions. Recently, deep learning has
catalyzed a paradigm shift in 3D reconstruction. A new family of models,
exemplified by DUSt3R, has pioneered a feed-forward approach. These models
employ a unified deep network to jointly infer camera poses and dense geometry
directly from an Unconstrained set of images in a single forward pass. This
survey provides a systematic review of this emerging domain. We begin by
dissecting the technical framework of these feed-forward models, including
their Transformer-based correspondence modeling, joint pose and geometry
regression mechanisms, and strategies for scaling from two-view to multi-view
scenarios. To highlight the disruptive nature of this new paradigm, we contrast
it with both traditional pipelines and earlier learning-based methods like
MVSNet. Furthermore, we provide an overview of relevant datasets and evaluation
metrics. Finally, we discuss the technology's broad application prospects and
identify key future challenges and opportunities, such as model accuracy and
scalability, and handling dynamic scenes.
