融合视觉与几何先验，提升复杂场景单视角3D重建精度
MGP-KAD: Multimodal Geometric Priors and Kolmogorov-Arnold Decoder for Single-View 3D Reconstruction in Complex Scenes
- 引入类级几何先验，动态调整以增强形状理解
- 采用KAN混合解码器，更好处理多模态输入的复杂关系
- 在Pix3D上实现当前最优性能，尤其改善细节与光滑性
复杂真实场景中的单视角3D重建面临噪声、物体多样性及数据集有限等挑战。为此，我们提出MGP-KAD框架，通过融合RGB图像与几何先验信息，提升重建准确性。几何先验基于真实物体数据采样与聚类生成，形成类级别特征，并在训练中动态调整，增强几何理解能力。同时，设计基于柯尔莫哥洛夫-阿诺德网络（KAN）的混合解码器，克服传统线性解码器对复杂多模态输入建模不足的问题。在Pix3D数据集上的大量实验表明，MGP-KAD达到当前最优（SOTA）表现，显著提升几何完整性、表面光滑度与细节保真度。本工作为复杂场景下的单视角3D重建提供了鲁棒有效的解决方案。
原文摘要 · Abstract (English)
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.
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