The demand for editable high-fidelity 3D content is increasing with the growth of immersive gaming and virtual reality. The \(\mathtt {M^3VIR}\) dataset was introduced as a large-scale multi-modal benchmark with accurate geometry and semantic annotations. This work extends the use of the dataset to two challenging settings: sparse-view novel-view synthesis (NVS) and 3D object removal. While the original dataset focused on dense observations, practical capture often involves limited viewpoints and scenes that require object editing. Pipelines that rely on Structure-from-Motion (SfM) for initialization frequently become unstable under sparse views and can produce incomplete geometry. We examine whether the annotations provided by \(\mathtt {M^3VIR}\) , including metric depth, precise camera poses, and instance-level semantic masks, can support reconstruction and editing under these constraints. For sparse-view NVS, we replace SfM initialization with a depth-based TSDF reconstruction that generates a geometric prior for neural rendering methods. This initialization improves stability for both 3D Gaussian Splatting and NeRFacto when only a small number of views are available. For object removal, we evaluate a mask-guided 2D-inpaint-to-3D-lift workflow in which objects are removed in individual views and the edited images are consolidated through multi-view optimization. Experimental results show that geometric and semantic annotations improve reconstruction stability in sparse-view settings and enable consistent scene editing after object removal. The study establishes baseline evaluation protocols for geometry-guided reconstruction and editing on multi-modal datasets.
