Recent Advances in Image-Based 3D Reconstruction: a Photogrammetric Perspective on Conventional and Learning-Based Techniques

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract Image-based 3D reconstruction is vital in many applications, such as digital twins, smart cities, machine vision, and autonomous driving. In recent years, it has undergone a paradigm shift, propelled by advancements in both conventional photogrammetry and deep learning. This review provides a comprehensive photogrammetric perspective on both conventional and learning-based techniques, a viewpoint that prioritizes geometric fidelity, robustness, handling of uncertainty, and suitability for real-world applications. We first systematically revisit the fundamentals of traditional pipelines: Structure from Motion (SfM), Multi-View Stereo (MVS), and surface reconstruction. The review then details recent progress in conventional methods, highlighting innovations in scalable and efficient SfM, specialized camera models for MVS, and robust surface reconstruction algorithms. Subsequently, we explore the transformative evolution brought by learning-based techniques, including deep SfM, learning-based MVS, differentiable rendering-based scene representation methods (NeRF, 3DGS), groundbreaking feed-forward 3D reconstruction models (e.g., DUSt3R, VGGT), and surface reconstruction including explicit and implicit methods. Emphasis is placed on evaluating whether learning-based approaches genuinely meet photogrammetric requirements such as metric accuracy and reliability, rather than optimizing solely for visual realism. Furthermore, we conclude by identifying key challenges and research frontiers including generalization across domains, scalability to high-resolution imagery, real-time performance, and uncertainty quantification. By bridging the gap between classical photogrammetry and data-driven 3D vision, this work aims to guide future research toward robust, accurate, and certifiable 3D reconstruction systems suitable for engineering, industrial, and geospatial applications.

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