  In recent years, neural implicit surface reconstruction has emerged as a
popular paradigm for multi-view 3D reconstruction. Unlike traditional
multi-view stereo approaches, the neural implicit surface-based methods
leverage neural networks to represent 3D scenes as signed distance functions
(SDFs). However, they tend to disregard the reconstruction of individual
objects within the scene, which limits their performance and practical
applications. To address this issue, previous work ObjectSDF introduced a nice
framework of object-composition neural implicit surfaces, which utilizes 2D
instance masks to supervise individual object SDFs. In this paper, we propose a
new framework called ObjectSDF++ to overcome the limitations of ObjectSDF.
First, in contrast to ObjectSDF whose performance is primarily restricted by
its converted semantic field, the core component of our model is an
occlusion-aware object opacity rendering formulation that directly
volume-renders object opacity to be supervised with instance masks. Second, we
design a novel regularization term for object distinction, which can
effectively mitigate the issue that ObjectSDF may result in unexpected
reconstruction in invisible regions due to the lack of constraint to prevent
collisions. Our extensive experiments demonstrate that our novel framework not
only produces superior object reconstruction results but also significantly
improves the quality of scene reconstruction. Code and more resources can be
found in \url{https://qianyiwu.github.io/objectsdf++}
