Abstract
We present an approach for object-level detection and segmentation of target indoor assets in 3D Gaussian Splatting (3DGS) scenes, reconstructed from 360° drone-captured imagery. We introduce a 3D object codebook that jointly leverages mask semantics and spatial information of their corresponding Gaussian primitives to guide multi-view mask association and indoor asset detection. By integrating 2D object detection and segmentation models with semantically and spatially constrained merging procedures, our method aggregates masks from multiple views into coherent 3D object instances. Experiments on two large indoor scenes demonstrate reliable multi-view mask consistency, improving F1 score by 65% over state-of-the-art baselines, and accurate object-level 3D indoor asset detection, achieving an 11% mAP gain over baseline methods.
Abstract (translated)
本文提出一种针对3D高斯泼溅（3DGS）场景中目标室内资产的目标级检测与分割方法，该场景由无人机360°航拍图像重建而成。我们引入了一个3D目标码本，联合利用高斯基元对应的掩码语义与空间信息，以引导多视角掩码关联与室内资产检测。通过整合2D目标检测与分割模型，并结合语义与空间约束的合并流程，我们的方法将多视角掩码聚合成连贯的3D目标实例。在两个大型室内场景上的实验表明，该方法实现了可靠的多视角掩码一致性，较当前最优基线方法F1分数提升65%，并实现了精确的目标级3D室内资产检测，较基线方法mAP提升11%。
URL
https://arxiv.org/abs/2604.05316
