Recent advances in neural reconstruction using posed image sequences have made remarkable progress. However, due to the lack of depth information, existing volumetric-based techniques simply duplicate 2D image features of the object surface along the entire camera ray. We contend this duplication introduces noise in empty and occluded spaces, posing challenges for producing high-quality 3D geometry. Drawing inspiration from traditional multi-view stereo methods, we propose an end-to-end 3D neural reconstruction framework CVRecon, designed to exploit the rich geometric embedding in the cost volumes to facilitate 3D geometric feature learning. Furthermore, we present Ray-contextual Compensated Cost Volume (RCCV), a novel 3D geometric feature representation that encodes view-dependent information with improved integrity and robustness. Through comprehensive experiments, we demonstrate that our approach significantly improves the reconstruction quality in various metrics and recovers clear fine details of the 3D geometries. Our extensive ablation studies provide insights into the development of effective 3D geometric feature learning schemes. Project page: https://cvrecon.ziyue.cool/

翻译：近年来，利用有姿态图像序列进行神经重建的研究取得了显著进展。然而，由于缺乏深度信息，现有基于体素的技术只是简单地将物体表面的二维图像特征沿整条相机射线复制。我们认为，这种复制在空区域和遮挡区域引入了噪声，给高质量三维几何的生成带来了挑战。受传统多视图立体方法的启发，我们提出了一个端到端的三维神经重建框架CVRecon，旨在于利用代价体中丰富的几何嵌入来促进三维几何特征学习。此外，我们提出了射线上下文补偿代价体（RCCV），一种新型的三维几何特征表示方法，它以更高的完整性和鲁棒性编码与视图相关的信息。通过全面的实验，我们证明了我们的方法在各种指标上显著提高了重建质量，并恢复了三维几何的清晰细节。我们广泛的消融研究为开发有效的三维几何特征学习方案提供了见解。项目页面：https://cvrecon.ziyue.cool/