Smart-Tree: Neural Medial Axis Approximation of Point Clouds for 3D Tree Skeletonization

In this paper, we present Smart-Tree, a supervised method for approximating the medial axes of branch skeletons from a tree's point cloud. A sparse voxel convolutional neural network extracts each input point's radius and direction towards the medial axis. A greedy algorithm performs robust skeletonization using the estimated medial axis. The proposed method provides robustness to complex tree structures and improves fidelity when dealing with self-occlusions, complex geometry, touching branches, and varying point densities. We train and test the method using a multi-species synthetic tree data set and perform qualitative analysis on a real-life tree point cloud. Experimentation with synthetic and real-world datasets demonstrates the robustness of our approach over the current state-of-the-art method. Further research will focus on training the method on a broader range of tree species and improving robustness to point cloud gaps. The details to obtain the dataset are at https://github.com/uc-vision/synthetic-trees.

Zero NeRF: Registration with Zero Overlap

We present Zero-NeRF, a projective surface registration method that, to the best of our knowledge, offers the first general solution capable of alignment between scene representations with minimal or zero visual correspondence. To do this, we enforce consistency between visible surfaces of partial and complete reconstructions, which allows us to constrain occluded geometry. We use a NeRF as our surface representation and the NeRF rendering pipeline to perform this alignment. To demonstrate the efficacy of our method, we register real-world scenes from opposite sides with infinitesimal overlaps that cannot be accurately registered using prior methods, and we compare these results against widely used registration methods.