Recent advances at the interface of machine learning and 3D reconstruction, ranging from volumetric methods like NeRF and Plenoxels to point-based primitives such as Gaussian Splats, have enabled high-fidelity 3D modeling from images. Despite this progress, capturing assets through the isolated optimization of segmented objects remains fundamentally under-constrained. Additionally, thin structures pose a difficult challenge for segmentation methods. In this work, we explore necessary constraints and combine priors from monocular depth as well as visual hulls to overcome their respective failure modes, producing high-quality object-centric reconstructions in the face of erroneous segmentation. This is demonstrated using synthetic scenes, all exhibiting fine structure, which we openly release along with coarse and ground truth segmentation masks. Furthermore, we show that segmentation failure can act as a useful signal to guide sampling and further enhance detail preservation.