CrossLiDAR: Controllable Multi-Sensor LiDAR Sequence Generation
Overview
This paper introduces CrossLiDAR, a unified transformer-based framework designed to generate high-fidelity, controllable, and spatio-temporally consistent multi-sensor LiDAR sequences. The system utilizes a rectified flow objective with a Multi-Modal Diffusion Transformer (MM-DiT) backbone within a compact latent space of range images. Key components include an Inter-sensor Module for cross-sensor interaction and a ControlNet-style branch for structural conditioning using BEV priors or 3D bounding boxes. To ensure long-horizon coherence, the model generates short N-frame sequences autoregressively. Evaluated on the nuScenes and Waymo datasets, CrossLiDAR achieves an average improvement of 29.56% in JSD and 15.36% in MMD compared to state-of-the-art baselines, addressing bottlenecks in scalable autonomous system development.
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- arXiv Robotics — research abstractsSignalCrossLiDAR: Controllable Multi-Sensor LiDAR Sequence Generation
This paper introduces CrossLiDAR, a unified transformer-based framework designed to generate high-fidelity, controllable, and spatio-temporally consistent multi-sensor LiDAR sequences. The system utilizes a rectified flow objective with a Multi-Modal Diffusion Transformer (MM-DiT) backbone within a compact latent space of range images. Key components include an Inter-sensor Module for cross-sensor interaction and a ControlNet-style branch for structural conditioning using BEV priors or 3D bounding boxes. To ensure long-horizon coherence, the model generates short N-frame sequences autoregressively. Evaluated on the nuScenes and Waymo datasets, CrossLiDAR achieves an average improvement of 29.56% in JSD and 15.36% in MMD compared to state-of-the-art baselines, addressing bottlenecks in scalable autonomous system development.
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