4 accepted papers
The growing adoption of compact and cost-effective solid-state LiDARs has greatly advanced robotics. However, their inherently limited Field-of-View (FOV) hinders their application in tasks requiring wide-range depth perception. To overcome this limitation, we introduce the Low Coverage Depth Comple
Test-Time Adaptive Segmentation (TTA-Seg) aims to adapt a trained segmentation model to test data under distribution shift in an unsupervised manner. Existing approaches typically utilize class-wise prototypes to capture and transfer the source distribution, but inevitably neglect the diversity with
Real-time dense depth completion has become a critical enabling technology for autonomous systems requiring instant environment perception, such as self-driving vehicles and robotics. While recent depth completion methods achieve remarkable accuracy, their computational complexity often exceeds the
Traditional vision transformer consists of two parts: transformer encoder and multi-layer perception (MLP). The former plays the role of feature learning to obtain better representation, while the latter plays the role of classification. Here, the MLP is constituted of two fully connected (FC) layer…