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Bosheng Li

Publications and source records attributed to Bosheng Li.

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Autonomous Orbit Determination Analysis of a Conceptual Cislunar Navigation Constellation based on Inter-Satellite Range Measurement

With the community's increasing interest in the cislunar space, building a navigation constellation servicing the whole cislunar space has become a pressing need. Previous studies mainly focus on constellations using orbits close to the Moon, which limits the servicing volume of the constellation. In this work, a four-satellite constellation using one L3 orbit, one L4 orbit, one L5 orbit and an orbit close to the Moon is proposed. The orbit determination accuracy is an important factor to be considered when designing parameters of the constellation. In this study, the mode of autonomous orbit determination (AOD) based on inter-satellite range data is considered. With such a model, the out-of-plane design parameters are identified as the main parameters influencing the AOD accuracy. For the AOD based on short arcs, we find that the increase of the out-of-plane amplitude can improve the AOD accuracy, and the out-of-plane initial phases have a more complex influence. A novel relative planarity factor (RPF) $P_\text{r}$, which has negative correlation with the AOD accuracy, is proposed as the metric to evaluate the variation of AOD performance. Using $P_\text{r}$, we demonstrate that the coplanarity of the constellation can significantly reduce the AOD accuracy. For the long arc AOD, the influence of different parameters is insignificant.

astro-ph.EP

Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors

We introduce Tree D-fusion, featuring the first collection of 600,000 environmentally aware, 3D simulation-ready tree models generated through Diffusion priors. Each reconstructed 3D tree model corresponds to an image from Google's Auto Arborist Dataset, comprising street view images and associated genus labels of trees across North America. Our method distills the scores of two tree-adapted diffusion models by utilizing text prompts to specify a tree genus, thus facilitating shape reconstruction. This process involves reconstructing a 3D tree envelope filled with point markers, which are subsequently utilized to estimate the tree's branching structure using the space colonization algorithm conditioned on a specified genus.

cs.CV

DeepTree: Modeling Trees with Situated Latents

In this paper, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model situated latent because its behavior is determined by the intrinsic state -- encoded as a latent space of a deep neural model -- and by the extrinsic (environmental) data that is situated as the location in the 3D space and on the tree structure. We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model. We use this representation to progressively develop new branch nodes, thereby mimicking the growth process of trees. Starting from a root node, a tree is generated by iteratively querying the neural network on the newly added nodes resulting in the branching structure of the whole tree. Our method enables generating a wide variety of tree shapes without the need to define intricate parameters that control their growth and behavior. Furthermore, we show that the situated latents can also be used to encode the environmental response of tree models, e.g., when trees grow next to obstacles. We validate the effectiveness of our method by measuring the similarity of our tree models and by procedurally generated ones based on a number of established metrics for tree form.

cs.LG