【摘要】 三维视觉领域正经历一场深刻的范式转变，核心问题正从单一的“感知重建”逐步迈向“重建—生成—交互”的一体化新阶段。本文旨在系统梳理三维重建与生成技术的前沿进展，对三维重建、三维生成及三维数字人等方向进行综述，剖析优化式与前馈式重建方法的原理差异，评估物体级生成、CAD(computer-aided design)生成及具身智能场景生成的现状与挑战，并对比2D与3D数字人技术在实时渲染与复杂交互中的表现。分析显示，三维重建技术中，优化式方法虽然精度占优但计算冗余，而前馈式方法虽然推断迅速但细节不足，两者融合及多模态语义注入是当前主流；三维生成领域中，技术焦点已从单纯的视觉质量转向部件级可控性，但CAD生成仍面临“脏几何”难以满足制造标准的难题；三维数字人技术方面，2D生成技术展现了非凡的生成能力与迭代速度，而3D技术在处理复杂空间交互时具有不可替代性。研究表明，三维领域正经历从“观测驱动重建”向“数据驱动生成”的范式转变，未来发展将集中在前馈式与优化式方法的深度融合、三维生成向工业可用性与可编辑性演进以及三维技术与具身智能、数字人等场景深度耦合三方面。未来，三维重建与生成将不再是孤立的视觉问题，而是支撑虚实融合与智能决策的基础能力。更多还原

【Abstract】 The field of 3D vision is currently undergoing a profound and historical paradigm shift, transitioning from a traditional core that focuses on “perception and reconstruction”, which emphasizes the faithful recovery of geometry from observation, to a new, integrated stage characterized by “reconstruction-generation-interaction”. This work provides a systematic, comprehensive, and critical review of frontier advances in 3D reconstruction and generation technologies, covering key directions that include 3D reconstruction paradigms, 3D object and scene generation, and the evolution of 3D digital humans. Specifically, it analyzes the fundamental principle differences between optimization-based and feed-forward reconstruction methods; evaluates the current status and bottlenecks of object-level generation, computer-aided design(CAD) generation, and embodied artificial intelligence(AI) scene generation; and compares the performance and future viability of 2D versus 3D digital human technologies in the context of real-time rendering and complex spatial interactions. The analysis indicates that in the domain of 3D reconstruction, two distinct technical paradigms have emerged, each with its unique strengths and limitations. Optimization-based methods, exemplified by classical pipelines, such as COLMAP, and modern neural representations, such as neural radiance fields and 3D Gaussian splatting, excel in achieving high-precision geometric recovery. By defining 3D representations as optimizable structures and iteratively minimizing the photometric error between rendered results and ground truth, these methods can achieve submillimeter-level accuracy on standard benchmarks, such as the Technical University of Denmark dataset. However, they suffer from significant computational redundancy, frequently requiring hundreds or thousands of iterations, and exhibit poor robustness when dealing with illposed problems, such as sparse-view input or textureless regions. By contrast, feed-forward reconstruction methods, represented by architectures such as VGGT, DUSt3R, and Fast3R, represent a shift toward data-driven direct prediction. These models utilize neural networks to predict 3D geometry from input images in a single forward pass, offering rapid inference speed and superior generalization capability in underdetermined conditions. However, they currently lack the fine-grained detail of optimization methods, often producing “blurry” geometry due to the domain gap between synthetic training data and real-world scenarios. The review identifies that the current mainstream trend is the deep fusion of the two paradigms, such as using feed-forward models to initialize optimization processes(e. g., Instant Splat), and the injection of multimodal semantic information. Notably, optimization methods excel at fusing pixel-aligned modalities, such as depth and normals, while feed-forward methods are more adept at integrating abstract semantic signals, such as text and audio. In the field of 3D generation, technical focus has shifted rapidly from pure visual quality to structural controllability and part-level manipulation. Although “3D native” large models have achieved success in generating high-resolution meshes, significant challenges remain in bridging the gap between AI generation and industrial production standards. An identified critical bottleneck is the specific domain of CAD generation. Although recent approaches have attempted to model CAD designs as sequence learning problems or graph-based B-rep generation, they frequently face the “dirty geometry” challenge. Generated models often exhibit non-watertight surfaces, irregular topology, and defective assembly relations, failing to meet the strict constraints required for manufacturing, physics simulation, or downstream engineering tasks. Furthermore, this study highlights the intersection of 3D generation and embodied AI. For robotic simulation and training, the demand is not merely for static visual assets but for interactive scenes wherein objects possess articulated parts, physical properties, and realistic but messy layouts. Current generative methods struggle to produce such complex, unordered scenes that reflect the reality of the physical world. However, such condition is essential for training robust embodied agents. With regard to digital human technology, the field is witnessing a fierce competition between 2D and 3D approaches. 2D generation methods, which are driven by advanced video diffusion models(e. g., Sora, Animate Anyone), have demonstrated tremendous progress, delivering hyperrealistic visuals and rapid iteration speed that challenge the necessity of traditional 3D pipelines. These 2D models leverage massive video datasets to learn motion and appearance, frequently bypassing the need for explicit geometric modeling. However, the analysis argues that 3D technology remains irreplaceable in handling complex spatial interactions. 3D digital humans(e. g., Gaussian avatars) provide the precise geometric consistency, explicit depth information, and collision data that are necessary for immersive virtual reality/augmented reality experiences and autonomous system interactions—capabilities that 2D video generation currently lacks. This study suggests a future trend where 2D and 3D technologies converge, with massive 2D video data being used to supervise and refine 3D representations, moving toward “multimodal interactive avatars” that are capable of understanding and generating speech, gesture, and expression in real-time environments. This study reveals that the 3D field is experiencing a fundamental paradigm shift from “observation-driven reconstruction” to “data-driven generation”. Future developments will likely focus on three strategic directions: 1) the deep fusion of feed-forward and optimization-based methods to solve the inherent trade-off between robustness and precision; 2) the evolution of 3D generation toward industrial usability and editability, ensuring that assets are physically valid and topologically sound rather than merely visually plausible; and 3) the deep coupling of 3D technology with scenarios, such as embodied AI and digital humans, where generation serves functional interaction needs. Ultimately, 3D reconstruction and generation are no longer isolated visual problems but have evolved into foundational capabilities that support virtual-real fusion and intelligent decision-making.更多还原