POTF: Prompt-based Object-centric Tensorial Field

Recent advancements reconstructing a 3D scene, particularly Neural Radiance Field (NeRF), have significantly en-hanced 3D scene synthesis through continuous implicit functions for volume rendering. However, real-world applications often require the reconstruction of specific regions or objects within a scene due to practical considerations such as computational efficiency and data storage limitations. To address these needs, we present Prompt-based Object-centric Tensorial Field (POTF), a novel approach that integrates user-directed segmentation via the Grounded Segment Anything Model (Grounded SAM) with efficient rendering capabilities utilizing tensor decomposition techniques. Our model allows precise segmentation based on image and text inputs, enabling dynamic and interactive refine-ment of the target regions. The tensor decomposition significantly improves reconstruction speed and quality by efficiently repre-sent the 3D radiance fields. POTF prioritizes the rendering of user-specified objects, optimizing computational resources while maintaining high fidelity in critical areas. This method offers a practical and adaptive solution for 3D scene synthesis, catering to the specific needs of various real-world applications.