Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Covering

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user-specified parameter $r$ (a small positive real number) as input and extracts an iso-surface with an iso-value $r$ using the conventional marching cubes algorithm. We show that the computed iso-surface is the boundary of the $r$-offset volume of the target zero level-set $S$, which is an orientable manifold, regardless of the topology of $S$. Next, the algorithm computes a covering map to project the boundary mesh onto $S$, preserving the mesh's topology and avoiding folding. If $S$ is an orientable manifold surface, our algorithm separates the double-layered mesh into a single layer using a robust minimum-cut post-processing step. Otherwise, it keeps the double-layered mesh as the output. We validate our algorithm by reconstructing 3D surfaces of open models and demonstrate its efficacy and effectiveness on synthetic models and benchmark datasets. Our experimental results confirm that our method is robust and produces meshes with better quality in terms of both visual evaluation and quantitative measures than existing UDF-based methods. The source code is available at https://github.com/jjjkkyz/DCUDF.

Improved Real-time Image Smoothing with Weak Structures Preserved and High-contrast Details Removed

Image smoothing is by reducing pixel-wise gradients to smooth out details. As existing methods always rely on gradients to determine smoothing manners, it is difficult to distinguish structures and details to handle distinctively due to the overlapped ranges of gradients for structures and details. Thus, it is still challenging to achieve high-quality results, especially on preserving weak structures and removing high-contrast details. In this paper, we address this challenge by improving the real-time optimization-based method via iterative least squares (called ILS). We observe that 1) ILS uses gradients as the independent variable in its penalty function for determining smoothing manners, and 2) the framework of ILS can still work for image smoothing when we use some values instead of gradients in the penalty function. Thus, corresponding to the properties of pixels on structures or not, we compute some values to use in the penalty function to determine smoothing manners, and so we can handle structures and details distinctively, no matter whether their gradients are high or low. As a result, we can conveniently remove high-contrast details while preserving weak structures. Moreover, such values can be adjusted to accelerate optimization computation, so that we can use fewer iterations than the original ILS method for efficiency. This also reduces the changes onto structures to help structure preservation. Experimental results show our advantages over existing methods on efficiency and quality.

NeUDF: Learning Unsigned Distance Fields from Multi-view Images for Reconstructing Non-watertight Models

Volume rendering-based 3D reconstruction from multi-view images has gained popularity in recent years, largely due to the success of neural radiance fields (NeRF). A number of methods have been developed that build upon NeRF and use neural volume rendering to learn signed distance fields (SDFs) for reconstructing 3D models. However, SDF-based methods cannot represent non-watertight models and, therefore, cannot capture open boundaries. This paper proposes a new algorithm for learning an accurate unsigned distance field (UDF) from multi-view images, which is specifically designed for reconstructing non-watertight, textureless models. The proposed method, called NeUDF, addresses the limitations of existing UDF-based methods by introducing a simple and approximately unbiased and occlusion-aware density function. In addition, a smooth and differentiable UDF representation is presented to make the learning process easier and more efficient. Experiments on both texture-rich and textureless models demonstrate the robustness and effectiveness of the proposed approach, making it a promising solution for reconstructing challenging 3D models from multi-view images.