EnerGS: 基于部分几何先验的能量场能量基于高斯点散射
EnerGS: Energy-Based Gaussian Splatting with Partial Geometric Priors
AI总结：
EnerGS通过建模部分可观测几何为连续能量场，提升大规模户外场景中高斯点散射的光度质量和几何稳定性，同时缓解过拟合问题。
摘要：
3D高斯点散射（3DGS）已被广泛用于场景重建，其中训练本质上是一个高度耦合且非凸的优化问题。近期工作通常结合几何先验，如激光雷达测量，用于初始化或作为训练约束，以提高光度重建质量。然而，在大规模户外场景中，此类几何监督往往空间不完整且不均匀，限制了其作为可靠先验的有效性，甚至可能损害最终重建。为解决这一挑战，我们建模部分可观测几何为由几何证据诱导的连续能量场，并提出EnerGS。与强制几何为硬约束不同，EnerGS为高斯原始体的优化提供软几何指导，允许几何信息引导优化过程，而无需直接限制解空间。在大规模户外场景上的大量实验表明，在稀疏多视角和单目设置下，EnerGS一致提高了光度质量和几何稳定性，同时有效缓解了3DGS训练中的过拟合问题。
英文摘要：
3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent works commonly incorporate geometric priors, such as LiDAR measurements, either for initialization or as training constraints, with the goal of improving photometric reconstruction quality. However, in large-scale outdoor scenarios, such geometric supervision is often spatially incomplete and uneven, which limits its effectiveness as a reliable prior and can even be detrimental to the final reconstruction. To address this challenge, we model partially observable geometry as a continuous energy field induced by geometric evidence and propose EnerGS. Rather than enforcing geometry as a hard constraint, EnerGS provides a soft geometric guidance for the optimization of Gaussian primitives, allowing geometric information to steer the optimization process without directly restricting the solution space. Extensive experiments on large-scale outdoor scenes demonstrate that, under both sparse multi-view and monocular settings, EnerGS consistently improves photometric quality and geometric stability, while effectively mitigating overfitting during 3DGS training.
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