GenLaM: Generative Layered Mesh for Multi-Modal Sensor Emulation in Robotics

Abstract

Accurate environment perception is fundamental for robust robot navigation, mapping, and interaction. Traditional perception pipelines rely on multiple sensors, including stereo cameras and LiDAR, which impose constraints on cost, payload, and system integration. In this paper, we propose a novel single-image perception framework that unifies novel view synthesis and RGB/segmented LiDAR emulation into a single pipeline. Leveraging monocular depth estimation and camera intrinsics recovery, our approach projects image pixels into 3D space and performs mesh reconstruction to generate dense geometric representations. This enables high-fidelity sensor emulation, including transparent surface reconstruction such as glass - an element often missed by conventional LiDAR. By enriching synthetic LiDAR scans with otherwise unavailable geometry, our method enhances downstream tasks such as robot path planning and obstacle avoidance. This work opens up new possibilities for resource-efficient robotic perception by reducing sensor dependency while improving geometric reasoning.