Title: Remove360: Benchmarking Residuals After Object Removal in 3D Gaussian Splatting
Primary area: datasets and benchmarks
Keywords: Neural 3D Reconstruction, Computer Vision, Semantic Segmentation

Abstract: Understanding what semantic information persists after object removal is critical for privacy-preserving 3D reconstruction and editable scene representations. In this work, we introduce a novel benchmark and evaluation framework to measure semantic residuals—the unintended semantic traces left behind—after object removal in 3D Gaussian Splatting. We conduct experiments across a diverse set of indoor and outdoor scenes, showing that current methods can preserve semantic information despite the absence of visual geometry. We also release Remove360, a dataset of pre/post-removal RGB images and object-level masks captured in real-world environments. While prior datasets have focused on isolated object instances, Remove360 covers a broader and more complex range of indoor and outdoor scenes, enabling evaluation of object removal in the context of full-scene representations. Given ground truth images of a scene before and after object removal, we assess whether we can truly eliminate semantic presence, and if downstream models can still infer what was removed. Our findings reveal critical limitations in current 3D object removal techniques and underscore the need for more robust solutions capable of handling real-world complexity.

TL;DR: This work introduces a benchmark and dataset to evaluate traces after 3D object removal, revealing limitations in current editing methods.