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1. DeepPCD: Enabling AutoCompletion of Indoor Point Clouds with Deep Learning

   https://doi.org/10.1145/3534611  

   Cai, Pingping; Sur, Sanjib (July 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   3D Point Cloud Data (PCD) is an efficient machine representation for surrounding environments and has been used in many applications. But the measured PCD is often incomplete and sparse due to the sensor occlusion and poor lighting conditions. To automatically reconstruct complete PCD from the incomplete ones, we propose DeepPCD, a deep-learning-based system that reconstructs both geometric and color information for large indoor environments. For geometric reconstruction, DeepPCD uses a novel patch based technique that splits the PCD into multiple parts, approximates, extends, and independently reconstructs the parts by 3D planes, and then merges and refines them. For color reconstruction, DeepPCD uses a conditional Generative Adversarial Network to infer the missing color of the geometrically reconstructed PCD by using the color feature extracted from incomplete color PCD. We experimentally evaluate DeepPCD with several real PCD collected from large, diverse indoor environments and explore the feasibility of PCD autocompletion in various ubiquitous sensing applications. 

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2. Suicidal selection: Programmed cell death can evolve in unicellular organisms due solely to kin selection

   https://doi.org/10.1002/ece3.5460  

   Vostinar, Anya_E; Goldsby, Heather_J; Ofria, Charles (July 2019, Ecology and Evolution)

   AbstractUnicellular organisms can engage in a process by which a cell purposefully destroys itself, termed programmed cell death (PCD). While it is clear that the death of specific cells within amulticellularorganism could increase inclusive fitness (e.g., during development), the origin of PCD inunicellularorganisms is less obvious. Kin selection has been shown to help maintain instances of PCD in existing populations of unicellular organisms; however, competing hypotheses exist about whether additional factors are necessary to explain its origin. Those factors could include an environmental shift that causes latent PCD to be expressed, PCD hitchhiking on a large beneficial mutation, and PCD being simply a common pathology. Here, we present results using an artificial life model to demonstrate that kin selection can, in fact, be sufficient to give rise to PCD in unicellular organisms. Furthermore, when benefits to kin are direct—that is, resources provided to nearby kin—PCD is more beneficial than when benefits are indirect—that is, nonkin are injured, thus increasing the relative amount of resources for kin. Finally, when considering how strict organisms are in determining kin or nonkin (in terms of mutations), direct benefits are viable in a narrower range than indirect benefits. Open Research BadgesThis article has been awarded Open Data and Open Materials Badges. All materials and data are publicly accessible via the Open Science Framework athttps://github.com/anyaevostinar/SuicidalAltruismDissertation/tree/master/LongTerm. 

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3. Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis

   Zhao, Qitao; Tulsiani, Shubham (December 2024, Conference and Workshop on Neural Information Processing Systems)

   Inferring the 3D structure underlying a set of multi-view images typically requires solving two co-dependent tasks -- accurate 3D reconstruction requires precise camera poses, and predicting camera poses relies on (implicitly or explicitly) modeling the underlying 3D. The classical framework of analysis by synthesis casts this inference as a joint optimization seeking to explain the observed pixels, and recent instantiations learn expressive 3D representations (e.g., Neural Fields) with gradient-descent-based pose refinement of initial pose estimates. However, given a sparse set of observed views, the observations may not provide sufficient direct evidence to obtain complete and accurate 3D. Moreover, large errors in pose estimation may not be easily corrected and can further degrade the inferred 3D. To allow robust 3D reconstruction and pose estimation in this challenging setup, we propose SparseAGS, a method that adapts this analysis-by-synthesis approach by: a) including novel-view-synthesis-based generative priors in conjunction with photometric objectives to improve the quality of the inferred 3D, and b) explicitly reasoning about outliers and using a discrete search with a continuous optimization-based strategy to correct them. We validate our framework across real-world and synthetic datasets in combination with several off-the-shelf pose estimation systems as initialization. We find that it significantly improves the base systems' pose accuracy while yielding high-quality 3D reconstructions that outperform the results from current multi-view reconstruction baselines. 

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4. Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems

   https://doi.org/10.1609/aaai.v33i01.33011528  

   Kazemi, E.; Wang, L. (January 2019, Proceedings of the AAAI Conference on Artificial Intelligence)

   Nonconvex and nonsmooth problems have recently attracted considerable attention in machine learning. However, developing efficient methods for the nonconvex and nonsmooth optimization problems with certain performance guarantee remains a challenge. Proximal coordinate descent (PCD) has been widely used for solving optimization problems, but the knowledge of PCD methods in the nonconvex setting is very limited. On the other hand, the asynchronous proximal coordinate descent (APCD) recently have received much attention in order to solve large-scale problems. However, the accelerated variants of APCD algorithms are rarely studied. In this project, we extend APCD method to the accelerated algorithm (AAPCD) for nonsmooth and nonconvex problems that satisfies the sufficient descent property, by comparing between the function values at proximal update and a linear extrapolated point using a delay-aware momentum value. To the best of our knowledge, we are the first to provide stochastic and deterministic accelerated extension of APCD algorithms for general nonconvex and nonsmooth problems ensuring that for both bounded delays and unbounded delays every limit point is a critical point. By leveraging Kurdyka-Łojasiewicz property, we will show linear and sublinear convergence rates for the deterministic AAPCD with bounded delays. Numerical results demonstrate the practical efficiency of our algorithm in speed. 

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5. Bird-view 3D Reconstruction for Crops with Repeated Textures

   Guoyu Lu (January 2023, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Large-scale in-situ 3D reconstruction of crop fields presents a challenging task, as the 3D crop structures play a crucial role in plant phenotyping and significantly influence crop growth and yield. While existing efforts focus on close range plants, only a limited number of deep learning-based methods have been developed explicitly for large-scale 3D crop reconstruction, mainly due to the scarcity of large-scale crop sensing data. In this paper, we leverage unmanned aerial vehicles (UAVs) in agriculture and utilize a recently captured multiview real-world snap beans crop dataset to develop an unsupervised structure-from-motion (SfM) framework. Our framework is designed specifically for reconstructing large-scale 3D crop structures. It addresses the challenge of inaccurate depth inference caused by excessively repeated patterns in the crop dataset, resulting in highly accurate 3D crop reconstruction for large-scale scenarios. Through experiments conducted on the crop dataset, we demonstrate the accuracy and robustness of our 3D crop reconstruction algorithm. The application of our proposed framework has the potential to advance research in agriculture, enabling better plant phenotyping and understanding of crop growth and yield. 

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