用混合几何先验提升水下多视角图像的神经SDF重建质量
UW-SDF: Exploiting Hybrid Geometric Priors for Neural SDF Reconstruction from Underwater Multi-view Monocular Images
- 引入混合几何先验优化神经SDF重建流程
- 在多个数据集上优于传统方法和现有神经渲染模型
- 支持少样本快速分割未见物体，适合水下三维重建场景
由于水下环境的独特特性，水下物体的精确三维重建在水下探测与测绘任务中面临挑战。依赖多传感器数据的传统重建方法耗时且难以获取水下数据。本文提出UW-SDF框架，基于神经SDF从多视角水下单目图像重建目标物体。引入混合几何先验优化重建过程，显著提升神经SDF重建的质量与效率。为解决多视角图像中分割一致性难题，提出一种基于通用分割模型（SAM）的少样本多视角目标分割策略，实现对未见物体的快速自动分割。在多个数据集上的大量定性与定量实验表明，所提方法在水下三维重建领域优于传统方法及其他神经渲染方法。
原文摘要 · Abstract (English)
Due to the unique characteristics of underwater environments, accurate 3D reconstruction of underwater objects poses a challenging problem in tasks such as underwater exploration and mapping. Traditional methods that rely on multiple sensor data for 3D reconstruction are time-consuming and face challenges in data acquisition in underwater scenarios. We propose UW-SDF, a framework for reconstructing target objects from multi-view underwater images based on neural SDF. We introduce hybrid geometric priors to optimize the reconstruction process, markedly enhancing the quality and efficiency of neural SDF reconstruction. Additionally, to address the challenge of segmentation consistency in multi-view images, we propose a novel few-shot multi-view target segmentation strategy using the general-purpose segmentation model (SAM), enabling rapid automatic segmentation of unseen objects. Through extensive qualitative and quantitative experiments on diverse datasets, we demonstrate that our proposed method outperforms the traditional underwater 3D reconstruction method and other neural rendering approaches in the field of underwater 3D reconstruction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。