VEnvision3D: A Synthetic Perception Dataset for 3D Multi-Task Model Research

Developing a unified multi-task foundation model has become a critical challenge in computer vision research. In the current field of 3D computer vision, most datasets only focus on single task, which complicates the concurrent training requirements of various downstream tasks. In this paper, we introduce VEnvision3D, a large 3D synthetic perception dataset for multi-task learning, including depth completion, segmentation, upsampling, place recognition, and 3D reconstruction. Since the data for each task is collected in the same environmental domain, sub-tasks are inherently aligned in terms of the utilized data. Therefore, such a unique attribute can assist in exploring the potential for the multi-task model and even the foundation model without separate training methods. Meanwhile, capitalizing on the advantage of virtual environments being freely editable, we implement some novel settings such as simulating temporal changes in the environment and sampling point clouds on model surfaces. These characteristics enable us to present several new benchmarks. We also perform extensive studies on multi-task end-to-end models, revealing new observations, challenges, and opportunities for future research. Our dataset and code will be open-sourced upon acceptance.

Optimized Design Method for Satellite Constellation Configuration Based on Real-time Coverage Area Evaluation

When using constellation synergy to image large areas for reconnaissance, it is required to achieve the coverage capability requirements with minimal consumption of observation resources to obtain the most optimal constellation observation scheme. With the minimum number of satellites and meeting the real-time ground coverage requirements as the optimization objectives, this paper proposes an optimized design of satellite constellation configuration for full coverage of large-scale regional imaging by using an improved simulated annealing algorithm combined with the real-time coverage evaluation method of hexagonal discretization. The algorithm can adapt to experimental conditions, has good efficiency, and can meet industrial accuracy requirements. The effectiveness and adaptability of the algorithm are tested in simulation applications.

Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression

Deep convolutional neural networks (CNNs) with a large number of parameters requires huge computational resources, which has limited the application of CNNs on resources constrained appliances. Decomposition-based methods, therefore, have been utilized to compress CNNs in recent years. However, since the compression factor and performance are negatively correlated, the state-of-the-art works either suffer from severe performance degradation or have limited low compression factors. To overcome these problems, unlike previous works compressing layers separately, we propose to compress CNNs and alleviate performance degradation via joint matrix decomposition. The idea is inspired by the fact that there are lots of repeated modules in CNNs, and by projecting weights with the same structures into the same subspace, networks can be further compressed and even accelerated. In particular, three joint matrix decomposition schemes are developed, and the corresponding optimization approaches based on Singular Values Decomposition are proposed. Extensive experiments are conducted across three challenging compact CNNs and 3 benchmark data sets to demonstrate the superior performance of our proposed algorithms. As a result, our methods can compress the size of ResNet-34 by 22x with slighter accuracy degradation compared with several state-of-the-art methods.