PartFull: A Hybrid Method for Part-Aware 3D Object Reconstruction from Sparse Views

Recent advancements in 3D object reconstruction have been significantly enhanced by generative models; however, challenges remain when detailed 3D shapes are reconstructed from limited, sparse views. Traditional methods often require multiple input views and known camera poses, whereas newer approaches that leverage diffusion models from single images encounter realworld data limitations. In response, we propose ''PartFull'', a novel framework for part-aware 3D reconstruction using a hybrid approach. ''PartFull'' generates realistic 3D models from sparse RGB images by combining implicit and explicit representations to optimize surface reconstruction. Starting with sketch-based 3D models from individual views, these models are fused into a coherent object. Our pipeline incorporates a pretrained latent space for part-aware implicit representations and a deformable grid for feature volume construction and surface optimization. PartFull's joint optimization of surface geometry, topology, and implicit part segmentation constitutes a new approach to addressing the challenges of 3D reconstruction from sparse views.