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，基于位姿图像序列的神经重建技术取得了显著进展。然而，由于缺乏深度信息，现有的基于体素的方法只是简单地将物体表面的2D图像特征沿整个相机光线复制。我们认为这种复制会在空域和遮挡空间中引入噪声，对生成高质量的3D几何结构构成挑战。受传统多视图立体方法的启发，我们提出了一个端到端的3D神经重建框架CVRecon，旨在利用代价体中的丰富几何嵌入来促进3D几何特征学习。此外，我们提出了光线上下文补偿代价体（RCCV），一种新颖的3D几何特征表示，它以更高的完整性和鲁棒性编码了视图相关信息。通过全面的实验，我们证明了我们的方法在多种指标上显著提升了重建质量，并恢复了3D几何结构的清晰精细细节。我们广泛的消融研究为开发有效的3D几何特征学习方案提供了见解。项目页面：https://cvrecon.ziyue.cool/