We present an automated method for modifying shapes to allow them to be produced by filament winding. The range of shapes where filament winding is applicable is limited due to the fact that the paths must follow (near-)geodesics on the surface without encountering concave regions. Our method allows us to take shapes with small protrusions or concavities that would prevent winding and modify them to similar shapes that are capable of being wound. We present a two-stage iterative approach for automatically making gradual modifications to a given shape to bring it into a reasonable condition, with the filament paths largely adhering to the surface and are roughly evenly covering the surface. In the push-out stage, we identify areas where filaments would be raised off of the surface during winding, developing a new approach for mapping between a raised filament and the surface it spans. In the pull-in stage, we identify regions where protrusions make it difficult to wind filaments over the surface. In both cases, we make local modifications to the surface (pulling in or pushing out the surface) to improve the likelihood of successfully winding in those areas, and generally make the arrangements of filaments more uniform. These stages are incorporated into an overall optimization process where repeated iterations are performed until winding is feasible everywhere. We present the results of this algorithm on several example shapes.

We study the integration of machine learning advice to improve upon traditional data structure designed for efficient search queries. Although there has been recent effort in improving the performance of binary search trees using machine learning advice, e.g., Lin et. al. (ICML 2022), the resulting constructions nevertheless suffer from inherent weaknesses of binary search trees, such as complexity of maintaining balance across multiple updates and the inability to handle partially-ordered or high-dimensional datasets. For these reasons, we focus on skip lists and KD trees in this work. Given access to a possibly erroneous oracle that outputs estimated fractional frequencies for search queries on a set of items, we construct skip lists and KD trees that provably provides the optimal expected search time, within nearly a factor of two. In fact, our learning-augmented skip lists and KD trees are still optimal up to a constant factor, even if the oracle is only accurate within a constant factor. We also demonstrate robustness by showing that our data structures achieves an expected search time that is within a constant factor of an oblivious skip list/KD tree construction even when the predictions are arbitrarily incorrect. Finally, we empirically show that our learning-augmented search data structures outperforms their corresponding traditional analogs on both synthetic and real-world datasets.

In recent years, novel view synthesis from a single image has seen significant progress thanks to the rapid advancements in 3D scene representation and image inpainting techniques. While the current approaches are able to synthesize geometrically consistent novel views, they often do not handle the view-dependent effects properly. Specifically, the highlights in their synthesized images usually appear to be glued to the surfaces, making the novel views unrealistic. To address this major problem, we make a key observation that the process of synthesizing novel views requires changing the shading of the pixels based on the novel camera, and moving them to appropriate locations. Therefore, we propose to split the view synthesis process into two independent tasks of pixel reshading and relocation. During the reshading process, we take the single image as the input and adjust its shading based on the novel camera. This reshaded image is then used as the input to an existing view synthesis method to relocate the pixels and produce the final novel view image. We propose to use a neural network to perform reshading and generate a large set of synthetic input-reshaded pairs to train our network. We demonstrate that our approach produces plausible novel view images with realistic moving highlights on a variety of real world scenes.