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New method simplifies image-to-point cloud registration using LiDAR upsampling

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel method for Image-to-Point Cloud Registration (I2P) that simplifies the integration of camera and LiDAR data. This technique generates a dense LiDAR intensity image from a sparse scan using Conditional Rectified Flow, which is then matched with a camera image. The system estimates the 6-DoF relative pose via PnP-RANSAC and can be fine-tuned with minimal LiDAR data, scaling to various sensor configurations. Experiments on the R3LIVE dataset demonstrated a mean error of 4.89° / 1.63 m and a registration time of approximately 0.68 seconds. AI

IMPACT This research could improve perception and autonomous systems by enhancing the accuracy and generalization of camera and LiDAR integration.

RANK_REASON The cluster contains a research paper detailing a new method for image-to-point cloud registration.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method simplifies image-to-point cloud registration using LiDAR upsampling

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Reon Tabata, Kenji Koide, Shuji Oishi, Masashi Yokozuka, Taku Okawara, Aoki Takanose, Jun Miura ·

    Image-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling

    arXiv:2607.14639v1 Announce Type: cross Abstract: Image-to-Point Cloud Registration (I2P) is essential for integrating camera and LiDAR in perception and autonomous systems, yet the modality gap between images and point clouds makes it difficult to achieve both high accuracy and …

  2. arXiv cs.CV TIER_1 English(EN) · Jun Miura ·

    Image-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling

    Image-to-Point Cloud Registration (I2P) is essential for integrating camera and LiDAR in perception and autonomous systems, yet the modality gap between images and point clouds makes it difficult to achieve both high accuracy and strong generalization. In this paper, we propose a…

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