arXiv:2607.02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse. We present DH-Active, a lightweight, training-free geometry back-end that treats the sensor as a metric ruler rather than the sole source of depth. Near-field returns anchor the metric relative pose of two views through PnP; visually trackable samples without a valid depth return are then triangulated under that pose. A parallax/reprojection gate abstains wherever the geometry is ill-conditioned,
The continuous improvement in consumer-grade depth sensors, like those in recent iPhones, is enabling novel approaches to 3D reconstruction and spatial awareness, pushing the boundaries of real-time geometry processing.
This development allows for more robust and accurate real-time understanding of physical environments using readily available hardware, which is critical for applications in robotics, augmented reality, and autonomous systems.
Current methods for real-time 3D geometry from consumer devices are often limited by sensor sparsity and range; this new approach optimizes for accuracy through selective abstention, significantly improving data utility.
- · Robotics companies
- · Augmented reality developers
- · Consumer electronics manufacturers
- · Computer vision researchers
- · Companies reliant on expensive, high-end 3D scanning equipment for basic tasks
- · Systems with poor real-time environmental awareness
Improved 3D environmental understanding will accelerate the development and deployment of autonomous systems.
More sophisticated and reliable AR/VR experiences will become widely accessible, integrating digital and physical worlds more seamlessly.
The enhanced ability of machines to perceive and interact with their physical environment could lead to breakthroughs in general-purpose AI and human-robot collaboration.
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Read at arXiv cs.AI