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Implicit Event-RGBD Neural SLAM
Delin Qu;Chi Yan;Dong Wang;Jie Yin;Qizhi Chen;Dan Xu;Yiting Zhang;Bin Zhao;Xuelong Li
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting variation, which often leads to issues like convergence failures, localization drifts, and distorted mapping. To address these challenges, we propose EN-SLAM, the first event-RGBD implicit neural SLAM framework, which eff...Show More
CodingSketch: A Hierarchical Sketch with Efficient Encoding and Recursive Decoding
2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Sketch is a probabilistic data structure widely used in various fields due to its high accuracy under small memory. Designing hierarchical data structures for real-world datasets with high skewness is one of the main optimization directions of Sketch. However, there is still a big accuracy gap between the existing sketches and the optimum. To fill the gap, we propose a new sketch called Coding Ske...Show More
Newton Sketches: Estimating Node Intimacy in Dynamic Graphs Using Newton's Law of Cooling
2024 IEEE 40th International Conference on Data Engineering (ICDE)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Dynamic graphs are gaining importance in many real-world applications based on different graph queries. Due to the large volume and high dynamicity, people resort to compute approximations to answer graph queries. However, previous work primarily evaluates the relationship between nodes based on frequency, which is not sufficient in many cases. We observe that this relationship varying process is ...Show More
ODTT: Optimized Dynamic Taxonomy Tree with Differential Privacy
2023 IEEE International Conference on Big Data (BigData)
Year: 2023 | Conference Paper |
For cybersecurity, privacy protection in big data has received more and more attention and research. Differential privacy is one of the important privacy protection methods, and our work pays attention to differential privacy based on the dynamic taxonomy tree, which can protect the publishing of set-valued data effectively. We propose the optimized dynamic taxonomy tree (ODTT) algorithm as a bett...Show More
HyperCalm Sketch: One-Pass Mining Periodic Batches in Data Streams
Zirui Liu;Chaozhe Kong;Kaicheng Yang;Tong Yang;Ruijie Miao;Qizhi Chen;Yikai Zhao;Yaofeng Tu;Bin Cui
2023 IEEE 39th International Conference on Data Engineering (ICDE)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batch...Show More
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