Efficient Interactive 3D Multi-Object Removal

2025-01-29

AI Summary

Existing 3D multi-object removal methods suffer from limited fine-grained control, inflexible interaction, and inconsistent multi-view inpainting. To address these limitations, this paper proposes the first interactive 3D multi-object editing framework tailored for real-world scenarios, enabling users to interactively define regions and selectively remove or retain specific objects. Methodologically, we introduce: (1) a mask matching and refinement module that integrates homography-based registration with high-confidence anchor-guided cross-view mask alignment; (2) an IoU-joint shape context distance loss to enhance geometric consistency; and (3) an end-to-end differentiable 3D inpainting pipeline with explicit multi-view consistency constraints. We further construct the first dedicated benchmark dataset for 3D multi-object removal. Experiments demonstrate that our method achieves >80% speedup over state-of-the-art approaches while maintaining comparable or superior reconstruction quality, significantly improving generalization and practical utility.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Object removal is of great significance to 3D scene understanding, essential for applications in content filtering and scene editing. Current mainstream methods primarily focus on removing individual objects, with a few methods dedicated to eliminating an entire area or all objects of a certain category. They however confront the challenge of insufficient granularity and flexibility for real-world applications, where users demand tailored excision and preservation of objects within defined zones. In addition, most of the current methods require kinds of priors when addressing multi-view inpainting, which is time-consuming. To address these limitations, we propose an efficient and user-friendly pipeline for 3D multi-object removal, enabling users to flexibly select areas and define objects for removal or preservation. Concretely, to ensure object consistency and correspondence across multiple views, we propose a novel mask matching and refinement module, which integrates homography-based warping with high-confidence anchor points for segmentation. By leveraging the IoU joint shape context distance loss, we enhance the accuracy of warped masks and improve subsequent inpainting processes. Considering the current immaturity of 3D multi-object removal, we provide a new evaluation dataset to bridge the developmental void. Experimental results demonstrate that our method significantly reduces computational costs, achieving processing speeds more than 80% faster than state-of-the-art methods while maintaining equivalent or higher reconstruction quality.

Problem

• 3D space multi-object removal
• precision control
• efficiency

Innovation

• 3D multi-object removal
• viewpoint consistency
• advanced repair technology