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As an interdisciplinary field that spans computer vision，graphics，artificial intelligence （AI），and optical imaging，3D vision serves as the cornerstone for constructing embodied general intelligence and the metaverse. The “scaling law” paradigm，upon which AI development relies，faces significantly diminishing marginal returns and encounters bottlenecks. Thus，the focus of academia and industry is pivoting ever more clearly toward foundational subjects that are closely related to 3D vision，such as “world models”，“spatial intelligence”，and “embodied intelligence”，granting 3D vision with unprecedented strategic attention and developmental opportunities. In 2025，the primary frontier trends in the field of 3D vision can be summarized as follows. 1） Feed-forward 3D reconstruction that supports spatiotemporal multi-image input： with breakthroughs in feed-forward 3D reconstruction technologies，such as visual geometry grounded Transformer（VGGT），obtaining scene structure and motion information through spatiotemporal multi-image feed-forward methods has become increasingly simple，exerting two profound effects. First，it provides a solid foundation to 3D scene understanding for spatial intelligence，allowing many traditional 2D vision problems to be solved more fundamentally in 3D space. Second，combined with efficient rendering technologies，such as 3D Gaussian splatting （3DGS），the threshold for high-quality 3D content production has been significantly lowered，paving the way for large-scale applications，such as digital twins and the metaverse. 2） Gradual fusion of 3D generation and 3D reconstruction： 3D AI generated content technologies，such as SAM3D support compositional and instance-level object generation under single-image input，with generation quality gradually reaching industrial-grade scanning standards while simultaneously integrating feed-forward reconstruction methods to gradually achieve the generation of authentic 3D structures and textures that are consistent with the input images. This condition will support the feed-forward multi-instance reconstruction of dynamic complex scenes，significantly improving real-time，multimodal perception and understanding capabilities in complex scenarios. 3） Integration of video generation and world models into embodied intelligence： Video generation technology is rapidly incorporating explicit or implicit 3D representations and evolving toward multi-view consistency，long sequences，and physical plausibility，directly driving the development of integrated “perception-generation-interaction” world model technologies. These types of world models，combined with feed-forward 3D reconstruction technology，will form a complete “multimodal perception-3D modeling-4D generation-real-time interaction” 4D world model. Simultaneously，world model methods have begun to serve embodied intelligence，and a unified framework of “understanding-generation-execution” has begun to emerge. World models are widely regarded by the academic community as the key path to achieving generalizable embodied intelligence and ultimately leading to artificial general intelligence. 4） Human behavior and video data becoming the core fuel that drives breakthroughs： Human operational spaces and interaction videos constitute a “data goldmine” for training embodied intelligence. The vast amount of human behavior videos on the internet，along with first-person perspective data collected through simple devices，contain physical common sense，causal reasoning，and interaction preferences that serve as the natural fuel for breaking through the current data bottlenecks of embodied intelligence. By performing explicit 3D perceptual reconstruction or latent space action alignment and learning on these data，a “data pyramid” base can be constructed to drive the scaling of embodied intelligence. 5） Evolution of the embodied training paradigm from imitation learning to interaction-driven reinforcement learning： The technical evolution of embodied intelligence vision-language-action （VLA） models is shifting from a supervised fine-tuning paradigm that relies on expert demonstrations to a composite training architecture that integrates online reinforcement learning. This shift effectively breaks the dependence on scarce high-quality data，enabling policies driven by sparse rewards to obtain generalization and exploration capabilities that surpass those of imitation learning，and solving the challenges of exploration and stable updates in continuous action spaces. Simultaneously，the development of high-performance training systems and action-conditioned world models provides infrastructure support for large-scale interaction data generation and efficient policy evolution，marking a new “post-training” stage for embodied intelligence that is centered on “interaction-driven” approaches. The top 10 selected research advancements of 2025 in the field of 3D vision include the following： 1） feed-forward 3D reconstruction that builds the foundation models for 3D vision （spatial intelligence）； 2） the convergence of the reconstruction and generation technical routes （video generation/3D generation），moving away from mutual assistance toward preliminary integration； 3） 3DGS/4DGS continuously improving representation efficiency，triggering a surge in scene modeling and volumetric video applications； 4） 3D generation： a shift from single-object visual realism to structuralized components/scenes and physical interactivity； 5） from video generation to world models： oriented toward spatiotemporal consistency，physical plausibility，and interactivity； 6） unified multimodal large models for understanding and generation，serving spatial intelligence perception； 7） frontier shifts in digital humans： from appearance modeling to multimodal interaction； 8） human data becoming the essential fuel for breaking through the scaling law of embodied intelligence； 9） embodied intelligence foundation models that are evolving toward unified models of integrated “understanding-imagination-execution”； and 10） “post-training” moment of embodied intelligence： the paradigm shift of VLA models from imitation learning to online reinforcement learning. Collectively，these breakthroughs have established the prototype of an integrated intelligent architecture characterized by “multimodal perception-3D modeling-4D generation-real-time interaction”，providing critical technical support for the substantive advancement of spatial and embodied intelligence. To promote academic discourse，this study extensively analyzes frontier trends in 3D vision and curates the top 10 annual research advances，offering valuable reference perspectives for academia and industry.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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