Object-centric surface reconstruction from multi-view images is crucial in creating editable digital assets for AR/VR. Due to the lack of geometric constraints, existing methods, e.g., NeuS necessitate annotating the object masks to reconstruct compact surfaces in mesh processing. Mask annotation, however, incurs considerable labor costs due to its cumbersome nature. This paper presents Hi-NeuS, a novel rendering-based framework for neural implicit surface reconstruction, aiming to recover compact and precise surfaces without multi-view object masks. Our key insight is that the overlapping regions in the object-centric views naturally highlight the object of interest as the camera orbits around objects. The object of interest can be specified by estimating the distribution of the rendering weights accumulated from multiple views, which implicitly identifies the surface that a user intends to capture. This inspires us to design a geometric refinement approach, which takes multi-view rendering weights to guide the signed distance functions (SDF) of neural surfaces in a self-supervised manner. Specifically, it retains these weights to resample a pseudo surface based on their distribution. This facilitates the alignment of the SDF to the object of interest. We then regularize the SDF's bias for geometric consistency. Moreover, we propose to use unmasked Chamfer Distance(CD) to measure the extracted mesh without post-processing for more precise evaluation. Our approach has been validated through NeuS and its variant Neuralangelo, demonstrating its adaptability across different NeuS backbones. Extensive benchmark on the DTU dataset shows that our method reduces surface noise by about 20%, and improves the unmasked CD by around 30%, achieving better surface details. The superiority of Hi-NeuS is further validated on BlendedMVS and handheld camera captures for content creation.

本研究针对现有表面重建方法需要进行对象掩膜标注的问题，提出了一种新的无掩膜神经隐式表面重建框架Hi-NeuS。该方法通过估计来自多视角的渲染权重分布，能够在无需掩膜的情况下精确恢复物体表面，并在DTU数据集上实现了表面噪声减少约20%和无掩膜Chamfer距离提高约30%的显著改进，展现了其在内容创作中的潜在影响。

高保真无掩膜神经表面重建用于虚拟现实