IDESplat: 迭代深度概率估计用于通用的3D高斯散射
IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting
AI总结：
IDESplat通过迭代深度概率估计提升3D高斯散射的通用性，利用多级DPBU单元消除单一 warp 的不稳定性，实现更精确的高斯均值预测。
摘要：
通用的3D高斯散射旨在通过前馈网络直接预测高斯参数进行场景重建。其中，高斯均值尤其难以预测，因此通常先估计深度再反投影以获得高斯球心。现有方法通常仅依赖单一 warp 估计深度概率，限制了其利用跨视角几何线索的能力，导致深度图不稳定且粗糙。为解决此问题，我们提出IDESplat，通过迭代应用 warp 操作提升深度概率估计以实现准确的高斯均值预测。首先，为消除单一 warp 的固有不稳定性，我们引入深度概率提升单元（DPBU），通过级联 warp 操作生成的极线注意力图以乘法方式整合。接着，通过堆叠多个DPBU构建迭代深度估计过程，逐步识别高概率的深度候选。随着IDESplat迭代提升深度概率估计并更新深度候选，深度图逐渐细化，从而获得准确的高斯均值。我们在RealEstate10K、ACID和DL3DV上进行了实验。IDESplat实现了出色的重建质量和最先进的性能，具有实时效率。在RE10K上，其PSNR优于DepthSplat 0.33 dB，仅使用10.7%的参数和70%的内存。此外，在跨数据集实验中，我们的IDESplat在DTU数据集上比DepthSplat的PSNR提高了2.95 dB，展示了其强大的泛化能力。
英文摘要：
Generalizable 3D Gaussian Splatting aims to directly predict Gaussian parameters using a feed-forward network for scene reconstruction. Among these parameters, Gaussian means are particularly difficult to predict, so depth is usually estimated first and then unprojected to obtain the Gaussian sphere centers. Existing methods typically rely solely on a single warp to estimate depth probability, which hinders their ability to fully leverage cross-view geometric cues, resulting in unstable and coarse depth maps. To address this limitation, we propose IDESplat, which iteratively applies warp operations to boost depth probability estimation for accurate Gaussian mean prediction. First, to eliminate the inherent instability of a single warp, we introduce a Depth Probability Boosting Unit (DPBU) that integrates epipolar attention maps produced by cascading warp operations in a multiplicative manner. Next, we construct an iterative depth estimation process by stacking multiple DPBUs, progressively identifying potential depth candidates with high likelihood. As IDESplat iteratively boosts depth probability estimates and updates the depth candidates, the depth map is gradually refined, resulting in accurate Gaussian means. We conduct experiments on RealEstate10K, ACID, and DL3DV. IDESplat achieves outstanding reconstruction quality and state-of-the-art performance with real-time efficiency. On RE10K, it outperforms DepthSplat by 0.33 dB in PSNR, using only 10.7% of the parameters and 70% of the memory. Additionally, our IDESplat improves PSNR by 2.95 dB over DepthSplat on the DTU dataset in cross-dataset experiments, demonstrating its strong generalization ability.
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。
