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：近期的神经重建研究利用姿态图像序列取得了显著进展。然而，由于缺乏深度信息，现有基于体素的技术仅沿整个相机射线复制物体表面的二维图像特征。我们认为这种复制在空区域和遮挡区域引入了噪声，对生成高质量的三维几何结构构成了挑战。受传统多视图立体方法的启发，我们提出了端到端的三维神经重建框架CVRecon，旨在利用代价体积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积（RCCV），这是一种新颖的三维几何特征表示方法，能够以更高的完整性和鲁棒性编码视角相关信息。通过全面的实验，我们证明了我们的方法在各项指标上显著提升了重建质量，并恢复了三维几何结构的清晰细节。广泛的消融研究为开发高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/