Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models

A new method for monocular person localization under camera ego-motion has been developed, addressing the challenges of accurately estimating a person's 3D position from 2D images captured by a moving camera. This approach utilizes a four-point model to jointly estimate the camera's 2D attitude and the person's 3D location, significantly improving localization accuracy compared to existing methods.

QueryOcc has been introduced as a query-based self-supervised framework that learns continuous 3D semantic occupancy directly from sensor data, addressing the challenges of 3D scene geometry and semantics in computer vision, particularly for autonomous driving applications.

A recent study has shown that semantic segmentation networks trained on specific lidar types struggle to generalize to new lidar systems without additional intervention. The research focuses on leveraging vision foundation models (VFMs) to enhance unsupervised domain adaptation for semantic segmentation of lidar point clouds, revealing key architectural insights for improving performance across different domains.