ReFineR: ポイントスプラッティングによるピクセルレベルの単一画像・密点群位置合わせ
ReFineR: Single image to dense point cloud registration at pixel level with point splatting
単一画像とおおよそのカメラ姿勢から、微分可能なポイントスプラッティングと勾配方向尺度を用いた解析合成最適化により、画像と点群をピクセルレベルで精密に位置合わせするオフラインフレームワークを提案。
詳しい要約
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著者: Ugo Insalaco, Jean-Emmanuel Deschaud, Pierre Dellenbach
分類: cs.CV
原文アブストラクト
Cameras and LiDARs (Light Detection and Ranging) provide complementary observations of the same scene: cameras capture rich appearance cues while LiDAR sensors provide accurate geometric measurements. In this work, we address the problem of fine image-to-point cloud registration from a single image and a roughly estimated camera pose. On robotic platforms, residual reprojection errors of several pixels are commonly observed even after calibration due to temporal desynchronization, mechanical vibrations, or inaccuracies in point cloud reconstruction. Such errors can lead to decimeter-level geometric discrepancies at moderate distances and significantly affect multiple downstream tasks, such as dense cross-modal supervision, texture mapping, and semantic label transfer. To address this challenge, we introduce ReFineR: an offline framework to finely register individual images to pre-reconstructed point clouds. ReFineR builds upon differentiable point splatting rasterization and a smooth Gradient Orientation Measure (GOM) to iteratively refine the camera pose through analysis-by-synthesis optimization. We evaluated the method on a curated dataset of manually calibrated images collected from heterogeneous data sources. Furthermore, we show that an automatic point cloud rescale factor can be estimated prior to optimization, enabling robust refinement across a wide range of scenarios using a single scale-invariant formulation. The project page can be found at https://refiner-project.github.io/
