Cross-Modal Benchmarking for Robotic Perception in Natural Environments
Researchers have introduced WildCross, a new benchmark designed to evaluate robotic perception systems in natural environments. The benchmark includes over 476,000 RGB frames with depth and surface normal annotations, along with pose and lidar data. This work expands on previous results, focusing on metric depth estimation to highlight the limitations of current vision models trained primarily on urban data. AI
IMPACT Highlights limitations in current AI models for real-world robotic applications, potentially driving development of more robust perception systems.