Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning
arXiv:2607.00514v1 Announce Type: cross Abstract: Automatic understanding of dynamic 4D point clouds, the 3D-point sequences captured over time by depth sensors and LiDAR, is central to robotics and embodied perception. Yet annotating them densely is expensive, making self-supervised pretraining the natural route to transferable representations. Existing pretext tasks, however, are almost entirely intra-modal, and the few methods that transfer knowledge from 2D foundation models rely on a single global embedding per clip, discarding the rich per-patch semantics that these models compute. To ad
The increasing sophistication of autonomous systems and the limitations of traditional 2D computer vision for real-world interactions drive the need for better 4D perception.
Improved 4D point cloud understanding is critical for robotics and embodied AI, enabling more robust navigation, manipulation, and interaction in complex environments.
This research introduces a method for distilling dense cross-modal correspondence from 2D foundation models into 4D point cloud representations, overcoming the limitations of intra-modal self-supervised learning.
- · Robotics companies
- · Autonomous vehicle developers
- · Embodied AI researchers
- · Depth sensor manufacturers
- · Companies relying solely on 2D vision for complex robotic tasks
- · Those with limited investment in 3D/4D data annotation infrastructure
More accurate and efficient perception systems for dynamic environments will emerge.
This could accelerate the commercial deployment of general-purpose humanoid robots and advanced autonomous systems.
Reduced reliance on extensive manual annotation for 4D data will lower development costs and broaden AI application areas.
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Read at arXiv cs.AI