6 accepted papers
Structure from Motion (SfM) estimates camera poses and reconstructs point clouds, forming a foundation for various tasks. However, applying SfM to driving scenes captured by multi-camera systems presents significant difficulties, including unreliable pose estimation, excessive outliers in road surfa…
Dynamic scene reconstruction for autonomous driving enables vehicles to perceive and interpret complex scene changes more precisely. Dynamic Neural Radiance Fields (NeRFs) have recently shown promising capability in scene modeling. However, many existing methods rely heavily on accurate poses inputs
Moving object segmentation plays a vital role in understanding dynamic visual environments. While existing methods rely on multi-frame image sequences to identify moving objects, single-image MOS is critical for applications like motion intention prediction and handling camera frame drops. However,
Sim-to-Real refers to the process of transferring policies learned in simulation to the real world, which is crucial for achieving practical robotics applications. However, recent Sim2real methods either rely on a large amount of augmented data or large learning models, which is inefficient for spec
Searching Gas Source actively and efficiently in unknown hazard environments is an important but challenging issue. Using mobile robots to autonomously search and navigate to gas source location provides a promising way. Existing methods are mostly based on the modularization framework which investi…
Soft continuum robot is of superiority in the applications in unstructured environments due to its high flexibility and safe interaction ability. Accurate shape control is regarded as one of the prerequisites to improve its practicability. Considering the situation where the 3D position signals are