Dehallu3D：通过循环视图一致性细化缓解幻觉的单图三维生成
Dehallu3D: Hallucination-Mitigated 3D Generation from a Single Image via Cyclic View Consistency Refinement
针对单图生成3D网格中的结构幻觉问题，提出即插即用的循环视图一致性优化模块，通过相邻一致性约束和自适应平滑约束在稠密中间视角上平衡几何连续性与细节保留，并引入离群风险度量（ORM）指标量化几何保真度，为3D生成质量评估提供新思路。
原文依据 we propose Dehallu3D for 3D mesh generation. Our key idea is to design a balanced multi-view continuity constraint
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论大规模三维重建模型已经彻底改变了三维内容生成领域，在虚拟现实和游戏等场景中实现了广泛应用。与其他大模型一样，大规模三维重建模型同样存在幻觉问题，会引入偏离输入数据的结构异常值（例如奇怪的孔洞或凸起）。然而，与其他大模型不同，大规模三维重建模型中的幻觉问题仍未得到充分探索，这导致三维打印物体畸形或虚拟场景沉浸感不足。此类幻觉主要源于现有方法从稀疏生成的多视图图像中重建三维内容，而这些图像存在较大的视角间隙和不连续性。为了通过消除异常值来缓解幻觉，我们提出了Dehallu3D用于三维网格生成。我们的核心思想是设计一种平衡的多视图连续性约束，在稠密的中间视角上强制实现平滑过渡，同时避免过度平滑可能抹去尖锐几何特征的问题。因此，Dehallu3D采用了一个即插即用的优化模块，包含两个关键约束：（i）相邻一致性，确保跨视角的几何连续性；（ii）自适应平滑，保留精细细节。我们进一步提出了离群风险度量（ORM）指标，从异常值的角度量化三维生成中的几何保真度。大量实验表明，Dehallu3D通过有效保留结构细节并去除幻觉异常值，实现了高保真三维生成。
英文原文摘要 1556 字符
Large 3D reconstruction models have revolutionized the 3D content generation field, enabling broad applications in virtual reality and gaming. Just like other large models, large 3D reconstruction models suffer from hallucinations as well, introducing structural outliers (e.g., odd holes or protrusions) that deviate from the input data. However, unlike other large models, hallucinations in large 3D reconstruction models remain severely underexplored, leading to malformed 3D-printed objects or insufficient immersion in virtual scenes. Such hallucinations majorly originate from that existing methods reconstruct 3D content from sparsely generated multi-view images which suffer from large viewpoint gaps and discontinuities. To mitigate hallucinations by eliminating the outliers, we propose Dehallu3D for 3D mesh generation. Our key idea is to design a balanced multi-view continuity constraint to enforce smooth transitions across dense intermediate viewpoints, while avoiding over-smoothing that could erase sharp geometric features. Therefore, Dehallu3D employs a plug-and-play optimization module with two key constraints: (i) adjacent consistency to ensure geometric continuity across views, and (ii) adaptive smoothness to retain fine details. We further propose the Outlier Risk Measure (ORM) metric to quantify geometric fidelity in 3D generation from the perspective of outliers. Extensive experiments show that Dehallu3D achieves high-fidelity 3D generation by effectively preserving structural details while removing hallucinated outliers.