解决水下3D重建中的散射与衰减问题，提升成像质量与定位精度。
Swimm3R: Splatting with Medium-aware SfM for Underwater 3D Reconstruction
- 融合介质感知SfM与贝塔样条点云，自适应建模水下成像特性。
- 在巴布亚多斯数据集上实现稳定海底结构重建，PSNR提升1.47dB。
- 适合水下机器人、海洋探测等场景的高精度三维重建任务。
我们提出Swimm3R，一种统一框架，结合介质感知结构从运动（SfM）与水下贝塔样条点云技术，解决水下3D重建中由散射和衰减引起的失效问题。Swimm3R将陆地几何先验融入前馈主干网络，并通过物理头回归水下图像形成参数、相机位姿及恢复后的点云。此外，我们引入水下贝塔样条点云技术，扩展高斯样条点云，使用贝塔基元与散射感知几何梯度，实现稳定的水下几何表示。我们进一步建立了巴布亚多斯水下视频数据集，验证方法在复杂水下环境中的有效性。在该数据集上，Swimm3R在强散射条件下稳健重建水下场景结构，生成连贯的海底几何。利用预测点云，所提出的水下贝塔样条点云在平均PSNR上比WaterSplatting提升1.47 dB，下游定位性能分别在RRA@15和RTA@15上提升2.0和2.4个百分点。
原文摘要 · Abstract (English)
We propose Swimm3R, a unified framework that combines medium-aware structure-from-motion (SfM) with Underwater Beta Splatting to address scattering- and attenuation-induced failures in underwater 3D reconstruction. Swimm3R distills in-air geometric priors into a feed-forward backbone and uses a physics head to regress underwater image-formation parameters, camera poses, and restored point clouds. Additionally, we introduce Underwater Beta Splatting, which extends Gaussian splatting with Beta primitives and scattering-aware geometric gradients for stable underwater geometry representation. We further establish the Barbados underwater video dataset to demonstrate the effectiveness of our method in challenging underwater environments. On this dataset, Swimm3R robustly recovers underwater scene structure under challenging scattering conditions, yielding coherent seafloor geometry. Using these predicted point clouds, the proposed Underwater Beta Splatting improves average PSNR by $1.47$ dB over WaterSplatting while increasing downstream localization performance by $2.0$ and $2.4$ percentage points in RRA@15 and RTA@15, respectively.
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