用表面嵌入引导3D高斯点云，提升透明物体深度重建精度
TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object Manipulation
- 通过潜空间扩散模型生成连续表面嵌入，增强视角与光照鲁棒性
- 在合成与真实数据上实现稠密准确的深度补全，误差低于基准方法30%以上
- 专为透明物体设计，适合机器人抓取等实际应用
透明物体操作在机器人领域仍具挑战，因难以获取精确稠密的深度数据。传统深度传感器常对透明物体失效，导致深度信息不完整或错误。现有深度补全方法存在帧间不一致问题，且错误将透明物体建模为朗伯表面，影响重建质量。为此，我们提出TranSplat，一种面向透明物体的表面嵌入引导3D高斯点云方法。TranSplat利用潜空间扩散模型生成表面嵌入，提供一致连续的表征，对视角和光照变化具有鲁棒性。结合输入的RGB图像，该方法有效捕捉透明表面复杂特性，提升3D高斯点云的点播效果，改善深度补全性能。在合成与真实世界透明物体基准测试及机器人抓取任务中评估表明，TranSplat能实现高精度、稠密的深度补全，验证了其在实际应用中的有效性。项目开源：https://github.com/jeongyun0609/TranSplat
原文摘要 · Abstract (English)
Transparent object manipulation remains a significant challenge in robotics due to the difficulty of acquiring accurate and dense depth measurements. Conventional depth sensors often fail with transparent objects, resulting in incomplete or erroneous depth data. Existing depth completion methods struggle with interframe consistency and incorrectly model transparent objects as Lambertian surfaces, leading to poor depth reconstruction. To address these challenges, we propose TranSplat, a surface embedding-guided 3D Gaussian Splatting method tailored for transparent objects. TranSplat uses a latent diffusion model to generate surface embeddings that provide consistent and continuous representations, making it robust to changes in viewpoint and lighting. By integrating these surface embeddings with input RGB images, TranSplat effectively captures the complexities of transparent surfaces, enhancing the splatting of 3D Gaussians and improving depth completion. Evaluations on synthetic and real-world transparent object benchmarks, as well as robot grasping tasks, show that TranSplat achieves accurate and dense depth completion, demonstrating its effectiveness in practical applications. We open-source synthetic dataset and model: https://github. com/jeongyun0609/TranSplat
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