Learning neural implicit surfaces from volume rendering has become popular for multi-view reconstruction. Neural surface reconstruction approaches can recover complex 3D geometry that are difficult for classical Multi-view Stereo (MVS) approaches, such as non-Lambertian surfaces and thin structures. However, one key assumption for these methods is knowing accurate camera parameters for the input multi-view images, which are not always available. In this paper, we present NoPose-NeuS, a neural implicit surface reconstruction method that extends NeuS to jointly optimize camera poses with the geometry and color networks. We encode the camera poses as a multi-layer perceptron (MLP) and introduce two additional losses, which are multi-view feature consistency and rendered depth losses, to constrain the learned geometry for better estimated camera poses and scene surfaces. Extensive experiments on the DTU dataset show that the proposed method can estimate relatively accurate camera poses, while maintaining a high surface reconstruction quality with 0.89 mean Chamfer distance.

NoPose-NeuS是一种将NeuS扩展到联合优化相机姿势、几何和颜色网络的神经隐式表面重建方法，通过将相机姿势编码为多层感知器(MLP)并引入多视图特征一致性和渲染深度损失，约束学习到的几何来获得更好的相机姿势和场景表面估计。DTU数据集上的广泛实验表明，该方法能够估计相对准确的相机姿势，同时保持高质量的表面重建，平均Chamfer距离为0.89。

NoPose-NeuS：联合优化相机姿态与神经隐式表面的多视图重建