3 accepted papers
Articulated objects are ubiquitous and important in robotics, AR/VR, and digital twins. Most self-supervised methods for articulated object modeling reconstruct discrete interaction states and relate them via cross-state geometric consistency, yielding representational fragmentation and drift that h…
Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused on supervised approaches, relying on annotated datasets to model articulated objects within limited categories. However,…
This paper introduces SemDA, a communication-efficient data aggregation method that uses distributed semantic communication for improved transmission and analysis. SemDA utilizes an end-to-end trainable network structure that reduces data transmission volume and deepens semantic feature aggregation.…