Key Points
1. The paper introduces InseRF, a method for generative object insertion in 3D scenes, addressing the challenge of inserting new objects into scenes based on user-provided textual description and a 2D bounding box in a reference viewpoint.
2. InseRF proposes grounding the 3D object insertion by a 2D view of the object inserted in one reference view of the scene and uses single-view object reconstruction methods to lift the 2D edit to 3D.
3. The proposed method is evaluated on various 3D scenes and is found to be effective compared to existing methods, capable of consistent generative object insertion without requiring explicit 3D information as input.
4. The paper discusses recent advances in novel view synthesis, generative modeling, and 3D scene editing, highlighting the limitations of existing methods for generating and inserting new objects in 3D scenes.
5. Evaluation of the proposed method demonstrates its ability to insert diverse objects in 3D scenes without the need for explicit 3D spatial guidance, addressing the shortcomings of existing 3D scene editing methods.
6. The paper provides a detailed explanation of the method's components such as grounding the 3D insertion using a reference 2D edit, estimating the 3D placement of the object through monocular depth estimation, and an optional refinement step to improve the inserted objects in the scenes.
Summary
The paper introduces InseRF, a novel method for generative object insertion in NeRF reconstructions of 3D scenes. The proposed solution addresses the challenge of generating new objects in 3D scenes by grounding 3D object insertion to a 2D object insertion in a reference view of the scene. This is achieved by lifting the 2D edit to 3D using a single-view object reconstruction method. InseRF is evaluated on various 3D scenes, demonstrating its capability for controllable and 3D-consistent object insertion without requiring explicit 3D information as input.
The paper discusses the limitations of existing methods for 3D scene editing and highlights the challenges in generating and inserting new objects in 3D scenes. The proposed InseRF method aims to enable 3D-consistent generative object insertion based on a textual description and a single-view 2D bounding box, which exceeds the capabilities of existing 3D scene editing methods. The method leverages a reference 2D edit to achieve 3D-consistent object insertion without requiring explicit 3D spatial guidance.
InseRF consists of several main steps, including grounding the 3D insertion using a reference 2D edit, lifting the 2D edit to 3D using single-view object reconstruction, estimating the 3D placement of the object using monocular depth estimation, optimizing the scale and distance of the object in the scene, and fusing the object and scene NeRF representations. Additionally, an optional refinement step is applied to the fused 3D representation to further improve the insertion.
The paper presents visual examples of the proposed method applied to different 3D scenes, demonstrating its capability to insert 3D-consistent objects and comparing it with existing baselines. It discusses the importance of scale and distance optimization, object density scaling, and the refinement step in achieving accurate and realistic object insertion.
The authors highlight that while their method is a general pipeline for generative object insertion, its performance is currently limited by the capabilities of underlying generative models. They also emphasize the potential for future improvements in such models to enhance the proposed pipeline.
Reference: https://arxiv.org/abs/2401.05335