4 accepted papers
Freespace detection in autonomous driving is limited by the lack of explicit geometric modeling, hindering generalization across complex terrains. Existing approaches are predominantly data-driven and neglect the physical structure of drivable surfaces. We propose Terrain Flat (TerrFlat), a physics-…
Image datasets with paired mean opinion scores (MOS) enable the quantization of the perceptual differences between images and are of great significance for images taken underwater. In our previous research, a pairwise label underwater image quality subjective ranking (PLUIQR) method was proposed. In…
Monocular Bird’s Eye View (BEV) semantic segmentation is critical for autonomous driving for its inherent advantages in spatial representation and downstream tasks. However, it is challenging to simultaneously learn view transformation and pixel-wise classification. Previous works suffer from non-fl…
In the pursuit of robust and generalizable environment perception and language understanding the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents hindering their performance in unseen environments. This paper introduces the generalized cross-modal…