Tidal flat environments present unique challenges for Three Dimensional (3D) reconstruction and multimodal data matching due to their complex geological structures. Traditional methods for 3D modeling struggle with sparse feature distributions, leading to inaccuracies in both geometry and appearance. This paper presents a novel algorithm that combines neural implicit representations with multimodal data fusion to address these challenges. We introduce an attention-based strategy for multimodal feature alignment. It effectively aligns fine-grained optical image features with the structural and intensity information derived from Light Detection And Ranging (LiDAR) ambient images. Our method uniquely integrates self-distilled vision transformer with no labels v2 (namely DINOv2)-guided attention for fusing optical and LiDAR data. Additionally, we leverage Signed Distance Field (SDF) priors to explicitly separate geometry and appearance features, enabling precise surface reconstruction. Experimental results demonstrate that our algorithm outperforms the existing methods, particularly in reconstructing fine details and adapting to complex environmental conditions. Future work will explore incorporating point cloud data and temporal modeling to enhance robustness in highly dynamic tidal flat environments.

Generally, there are two popular ways to protect image copyright, i.e., proactive protection (preventing illegal use via adversarial perturbation) and passive protection (verifying ownership by digital watermarking). However, since the perturbation and watermark embedded into an image will interfere with each other, directly embedding them into the image cannot achieve the proactive protection and passive protection, simultaneously. To address this issue, we propose an image copyright dual-protection approach, which embeds an Extractable and Imperceptible Adversarial Watermark (EIAW) in the image frequency-domain. Specifically, the adversarial watermark is automatically embedded and optimized in the manner of allowing for effectively attacking the Deep Neural Networks (DNNs) and accurately extracting the embedded watermark, simultaneously. Moreover, instead of using the pixel-domain constraints, i.e., $L_p$ norms, we introduce a frequency-domain constraint to optimize the watermark embedding locations. Experiments on ImageNet and CIFAR-10 demonstrate that the proposed EIAW achieves high attack effectiveness (up to 100%) and extraction accuracy (up to 93%), while maintaining good watermark imperceptibility.