# 3D视觉菜谱：数据、学习范式与应用

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-02 08:00
- AIHOT 分数：41
- AIHOT 链接：https://aihot.virxact.com/items/cmq5khxtw00hcsl38nc426c1z
- 原文链接：https://arxiv.org/abs/2606.04291

## AI 摘要

该论文提出了一种以数据为中心的3D视觉分类法，将几何表示（点云、网格、体素和3D高斯）、获取管道、数据集设计、基准构建和监督范式统一到单一概念图中。研究分析了2D监督3D学习、隐式神经表示和4D世界建模的最新进展，并厘清了重建、生成和视频建模等下游任务中表示、学习范式与效率－保真度平衡、多模态几何接地之间的关联。

## 正文

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 connects geometric 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, and 3D Gaussians--along with their acquisition pipelines. We then examine how dataset design, benchmark construction, and supervision regimes shape recent advances, spanning 2D-supervised 3D learning, implicit neural representations, and 4D world modeling. Through this integrative lens, we clarify the relationships among representations, learning paradigms, and downstream tasks in reconstruction, generation, and video modeling, offering a consolidated view of emerging trends toward balancing efficiency and fidelity and toward multimodal geometric grounding.