Recent large reconstruction models have made notable progress in generating high-quality 3D objects from single images. However, these methods often struggle with controllability, as they lack information from multiple views, leading to incomplete or inconsistent 3D reconstructions. To address this limitation, we introduce LucidFusion, a flexible end-to-end feed-forward framework that leverages the Relative Coordinate Map (RCM). Unlike traditional methods linking images to 3D world thorough pose, LucidFusion utilizes RCM to align geometric features coherently across different views, making it highly adaptable for 3D generation from arbitrary, unposed images. Furthermore, LucidFusion seamlessly integrates with the original single-image-to-3D pipeline, producing detailed 3D Gaussians at a resolution of 512×512, making it well-suited for a wide range of applications.
近年来，大型重建模型在从单张图像生成高质量3D对象方面取得了显著进展。然而，这些方法通常缺乏多视角信息，导致3D重建的可控性差、结构不完整或不一致。为了解决这一局限，我们提出了LucidFusion，这是一种灵活的端到端前馈框架，利用了相对坐标图（RCM）。不同于通过姿态将图像与3D世界关联的传统方法，LucidFusion利用RCM在不同视图间一致地对齐几何特征，使其在任意、无姿态的图像下生成3D对象时具备高度适应性。此外，LucidFusion无缝集成到原有的单图像转3D管道中，能够在512×512分辨率下生成精细的3D高斯表示，适用于多种应用场景。