6 accepted papers
Conventional imaging systems capture objects visible in the direct line-of-sight (LOS). A decade of research on non-line-of-sight (NLOS) imaging approaches has made it possible to reconstruct hidden geometry outside the line of sight by analyzing indirect light transport. However, most existing meth
Depth estimation is a cornerstone computer vision application that is critical for scene understanding and autonomous driving. In real-world scenarios, achieving reliable depth perception under adverse weather—e.g. in fog and rain—is crucial to ensure safety and system robustness. However, quantitat
Lidar has become crucial for autonomous driving, providing high-resolution 3D scans that are key for accurate scene understanding. To this end, lidar sensors measure the time-resolved full waveforms from the returning laser light, which a subsequent digital signal processor (DSP) converts to point c…
Lidar has become a cornerstone sensing modality for 3D vision especially for large outdoor scenarios and autonomous driving. Conventional lidar sensors are capable of providing centimeter-accurate distance information by emitting laser pulses into a scene and measuring the time-of-flight (ToF) of th…
LiDAR has become a cornerstone sensing modality for 3D vision. LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds…
Vision in adverse weather conditions, whether it be snow, rain, or fog is challenging. In these scenarios, scattering and attenuation severly degrades image quality. Handling such inclement weather conditions, however, is essential to operate autonomous vehicles, drones and robotic applications wher…