CASA-SDF：用于神经隐式曲面重建的基于曲率引导密度的课程感知空间自适应
CASA-SDF: Curriculum-Aware Spatial Adaptation with Curvature-Guided Density for Neural Implicit Surface Reconstruction
- School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
- School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对室内场景几何异质性带来的高保真曲面重建挑战，提出CASA-SDF框架。通过混合空间自适应不确定性退火构建课程进行先验监督，利用曲率感知局部自适应密度转换增强薄结构表示，实验证明该方法提升了高频结构曲面完整性和细节恢复，且不影响平面稳定性。
AI中文摘要：
神经隐式表示已成为三维重建的强大范例。然而，高保真室内曲面重建仍是重大挑战，主要因室内场景明显的几何异质性。大的无纹理平面区域通常需更强正则化抑制高频伪影，薄结构则需更锐利、自适应表示减轻多层感知器的频谱偏差并防止过度平滑。现有方法常依赖空间无差别先验监督和场景全局的符号距离函数到密度转换，限制了平衡平面平滑度和细节保留的能力。本文提出CASA-SDF，通过监督和表示能力的互补自适应应对挑战。具体而言，混合空间自适应不确定性退火融合语义和光度不确定性构建单眼先验监督的逐像素课程，在可靠区域维持正则化并在训练早期减弱不可靠监督以实现数据驱动的光度细化。同时，曲率感知局部自适应密度转换通过曲率代理逐步调制符号距离函数到密度映射的锐度以增强薄结构的表示。在基准室内数据集上的大量实验表明，CASA-SDF在不影响平面稳定性的情况下提高了高频结构的曲面完整性和细节恢复。
英文摘要：
Neural implicit representations have emerged as a powerful paradigm for 3D reconstruction. However, high-fidelity indoor surface reconstruction remains a significant challenge, primarily due to the pronounced \emph{geometric heterogeneity} of indoor scenes. Large texture-less planar regions typically require stronger regularization to suppress high-frequency artifacts, while thin structures demand sharper, more adaptive representations to mitigate the spectral bias of multi-layer perceptrons (MLPs) and prevent over-smoothing. Existing approaches often rely on spatially indiscriminate prior supervision and a scene-global SDF-to-density transformation, which constrains their ability to balance planar smoothness and detail preservation. In this paper, we propose CASA-SDF (Curriculum-Aware Spatial Adaptation for SDF), a unified framework that addresses this challenge via complementary adaptations of supervision and representation capacity. Specifically, Hybrid Spatially-Adaptive Uncertainty Annealing (SAUA) fuses semantic and photometric uncertainties to construct a pixel-wise curriculum for monocular prior supervision. This strategy maintains regularization in reliable regions while attenuating unreliable supervision early in training to enable data-driven photometric refinement. Meanwhile, Curvature-Aware Locally Adaptive Density Transformation (CALADT) progressively modulates the sharpness of the SDF-to-density mapping via a curvature proxy to enhance the representation of thin structures. Extensive experiments on benchmark indoor datasets demonstrate that CASA-SDF improves surface completeness and detail recovery on high-frequency structures, without compromising the stability of planar surfaces.
