We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps and refines camera poses guided by corresponding depth. Then, we adopt the neural implicit surface reconstruction method, which allows for high-quality mesh and develops a new training process for applying a regularization provided by classical multi-view stereo methods. Moreover, we apply a differentiable rendering to fine-tune incomplete texture maps and generate textures which are perceptually closer to the original scene. Our pipeline can be applied to any common objects in the real world without the need for either in-the-lab environments or accurate mask images. We demonstrate results of captured objects with complex shapes and validate our method numerically against existing 3D reconstruction and texture mapping methods.

本研究提出了一种新的流程来使用一部智能手机获取真实世界中带纹理的网格模型，使用RGBD辅助构建方法生成过滤的深度图和精确的相机姿势，核心技术采用神经隐式表达重建算法以及不同iable的渲染方法生成更加接近真实场景的纹理，并验证了该方法的性能对比其他3D重建+贴图算法。

使用可微分渲染技术，通过移动设备获取带纹理物体的网格模型