Anything-3D: Towards Single-view Anything Reconstruction in the Wild
This addresses the problem of single-view 3D reconstruction in diverse and complex environments for researchers and practitioners in computer vision, though it appears incremental as it builds on existing models like Segment-Anything and diffusion models.
The paper tackles 3D reconstruction from a single RGB image in unconstrained real-world scenarios by introducing Anything-3D, a framework that combines visual-language models and object segmentation to produce accurate and detailed 3D reconstructions for a wide array of objects.
3D reconstruction from a single-RGB image in unconstrained real-world scenarios presents numerous challenges due to the inherent diversity and complexity of objects and environments. In this paper, we introduce Anything-3D, a methodical framework that ingeniously combines a series of visual-language models and the Segment-Anything object segmentation model to elevate objects to 3D, yielding a reliable and versatile system for single-view conditioned 3D reconstruction task. Our approach employs a BLIP model to generate textural descriptions, utilizes the Segment-Anything model for the effective extraction of objects of interest, and leverages a text-to-image diffusion model to lift object into a neural radiance field. Demonstrating its ability to produce accurate and detailed 3D reconstructions for a wide array of objects, \emph{Anything-3D\footnotemark[2]} shows promise in addressing the limitations of existing methodologies. Through comprehensive experiments and evaluations on various datasets, we showcase the merits of our approach, underscoring its potential to contribute meaningfully to the field of 3D reconstruction. Demos and code will be available at \href{https://github.com/Anything-of-anything/Anything-3D}{https://github.com/Anything-of-anything/Anything-3D}.