MGP-KAD：多模态几何先验与科莫戈罗夫-阿诺德解码器用于复杂场景中的单视图3D重建
MGP-KAD: Multimodal Geometric Priors and Kolmogorov-Arnold Decoder for Single-View 3D Reconstruction in Complex Scenes
AI总结：
MGP-KAD通过整合多模态特征与几何先验，结合科莫戈罗夫-阿诺德网络解码器，提升复杂场景中单视图3D重建的精度与细节保持能力。
摘要：
单视图3D重建在复杂现实场景中具有挑战性，因为存在噪声、物体多样性以及数据集有限的问题。为了解决这些问题，我们提出了MGP-KAD，一种新的多模态特征融合框架，整合了RGB和几何先验以提高重建精度。几何先验通过采样和聚类真实物体数据生成，产生类级特征，在训练过程中动态调整以提高几何理解。此外，我们引入了一种基于科莫戈罗夫-阿诺德网络（KAN）的混合解码器，以克服传统线性解码器在处理复杂多模态输入时的限制。在Pix3D数据集上的广泛实验表明，MGP-KAD实现了最先进的性能，显著提高了几何完整性、平滑度和细节保持。我们的工作为推进复杂场景中的单视图3D重建提供了稳健且有效的解决方案。
英文摘要：
Single-view 3D reconstruction in complex real-world scenes is challenging due to noise, object diversity, and limited dataset availability. To address these challenges, we propose MGP-KAD, a novel multimodal feature fusion framework that integrates RGB and geometric prior to enhance reconstruction accuracy. The geometric prior is generated by sampling and clustering ground-truth object data, producing class-level features that dynamically adjust during training to improve geometric understanding. Additionally, we introduce a hybrid decoder based on Kolmogorov-Arnold Networks (KAN) to overcome the limitations of traditional linear decoders in processing complex multimodal inputs. Extensive experiments on the Pix3D dataset demonstrate that MGP-KAD achieves state-of-the-art (SOTA) performance, significantly improving geometric integrity, smoothness, and detail preservation. Our work provides a robust and effective solution for advancing single-view 3D reconstruction in complex scenes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
