BALTIC：跨空气和水域的3D重建基准与跨域策略
BALTIC: A Benchmark and Cross-Domain Strategy for 3D Reconstruction Across Air and Underwater Domains Under Varying Illumination
AI总结：
本文提出BALTIC基准，通过不同介质和光照条件下的多样化数据集评估3D重建方法，结合COLMAP和神经辐射场方法验证跨域重建性能，发现简单预处理的高斯点云在纹理一致条件下表现优异。
摘要：
在变化的环境条件下实现鲁棒的3D重建仍是机器人感知的关键挑战，尤其是在空气和水之间转换时。为此，我们引入BALTIC，一个受控基准，旨在系统评估现代3D重建方法在介质和光照变化下的表现。该基准包含13个数据集，覆盖两种介质（空气和水）和三种光照条件（环境、人工和混合），并进一步增加运动类型、扫描模式和初始化轨迹的变化，产生多样化的序列。我们的实验设置包括配备单目相机和HTC Vive追踪器的定制水箱，可实现准确的地面真实姿态估计。我们进一步通过在水下图像序列中添加少量在空气中的视图（在相似光照条件下拍摄）来研究跨域重建。我们使用COLMAP评估Structure-from-Motion重建的轨迹准确性和场景几何，并将这些重建作为Neural Radiance Fields和3D Gaussian Splatting方法的输入。生成的模型在地面真实轨迹和空气参考上进行评估，同时渲染输出使用感知和光度度量进行比较。此外，我们还进行了颜色恢复分析，以评估跨域的辐射一致性。我们的结果表明，在受控且纹理一致的条件下，简单的预处理（如白平衡校正）的高斯点云可以达到与专门设计的水下方法相当的性能，尽管其在更复杂和异质的现实环境中鲁棒性下降。
英文摘要：
Robust 3D reconstruction across varying environmental conditions remains a critical challenge for robotic perception, particularly when transitioning between air and water. To address this, we introduce BALTIC, a controlled benchmark designed to systematically evaluate modern 3D reconstruction methods under variations in medium and lighting. The benchmark comprises 13 datasets spanning two media (air and water) and three lighting conditions (ambient, artificial, and mixed), with additional variations in motion type, scanning pattern, and initialization trajectory, resulting in a diverse set of sequences. Our experimental setup features a custom water tank equipped with a monocular camera and an HTC Vive tracker, enabling accurate ground-truth pose estimation. We further investigate cross-domain reconstruction by augmenting underwater image sequences with a small number of in-air views captured under similar lighting conditions. We evaluate Structure-from-Motion reconstruction using COLMAP in terms of both trajectory accuracy and scene geometry, and use these reconstructions as input to Neural Radiance Fields and 3D Gaussian Splatting methods. The resulting models are assessed against ground-truth trajectories and in-air references, while rendered outputs are compared using perceptual and photometric metrics. Additionally, we perform a color restoration analysis to evaluate radiometric consistency across domains. Our results show that under controlled, texture-consistent conditions, Gaussian Splatting with simple preprocessing (e.g., white balance correction) can achieve performance comparable to specialized underwater methods, although its robustness decreases in more complex and heterogeneous real-world environments
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。
