A Cookbook of 3D Vision: Data, Learning Paradigms, and Application
Summary
This paper presents a comprehensive taxonomy of 3D vision research, covering geometric representations, datasets, learning paradigms, and applications in reconstruction, generation, and video modeling.
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Source: https://huggingface.co/papers/2606.04291 Published on Jun 2
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Abstract
3D vision research is organized through a taxonomy connecting geometric representations, datasets, learning frameworks, and applications across reconstruction, generation, and video modeling tasks.
3D vision has rapidly evolved, driven by increasingly diverse data representations, learning paradigms, and modeling strategies. Yet the field remains fragmented across representations and benchmarks, making it difficult to develop unified perspectives on efficiency, fidelity, and scalability. This work provides a data-centric taxonomy of 3D vision that connectsgeometric representations, datasets, learning frameworks, and applications within a single conceptual map. We begin by analysing the principal structural representations of 3D data--point clouds,meshes,voxels, and3D Gaussians--along with their acquisition pipelines. We then examine howdataset design,benchmark construction, andsupervision regimesshape recent advances, spanning2D-supervised 3D learning,implicit neural representations, and4D world modeling. Through this integrative lens, we clarify the relationships among representations, learning paradigms, and downstream tasks inreconstruction,generation, andvideo modeling, offering a consolidated view of emerging trends toward balancing efficiency and fidelity and towardmultimodal geometric grounding.
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