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}{this https URL}.

本文提出了 Anything-3D 框架，采用了一系列视觉语言模型和 Segment-Anything 对象分割模型来实现从单个 RGB 图像到 3D 重建的任务，并通过实验验证了其有效性和可靠性。