SpaRV: スケーラブルで幾何学的に一貫したLiDAR-視覚マッピングとレンダリングのためのスパース放射ボクセル
SpaRV: Sparse Radiance Voxels for Scalable Geometrically Consistent LiDAR-Visual Mapping and Rendering
LiDARスキャンと画像からスパースな放射ボクセルマップを構築し、微分可能レイマーチングで描画する手法。カメラ姿勢と焦点距離を最適化し、サブマップ管理で大規模シーンでもGPUメモリを抑える。
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著者: Jianheng Liu, Yunfei Wan, Fangming Cheng, Xiangyu Hong, Zuhao Zou, Haotian Li, Longji Yin, Chunran Zheng, Jiarong Lin, Fu Zhang
分類: cs.RO, cs.CV
原文アブストラクト
Robotic simulation, navigation, and inspection need photorealistic and geometrically accurate maps. Radiance fields achieve photorealism, but building them from robot data raises three challenges. A robot's trajectory serves its task, not reconstruction, so images from few viewpoints leave the geometry under-constrained. Residual errors in camera poses and intrinsics blur the map. The map's GPU memory grows with scene size, which limits its level of detail in large scenes. We present \textbf{SpaRV}, a \textbf{Spa}rse \textbf{R}adiance \textbf{V}oxel map rendered by differentiable ray marching, which lets irregular LiDAR scans supervise it alongside images. LiDAR depth and occupancy anchor the under-constrained geometry, and normal and smoothness regularizers keep it smooth between LiDAR points. To correct camera errors, SpaRV alternates map training with photometric optimization of the camera poses and focal lengths. To bound GPU memory in training and rendering, SpaRV holds one submap at a time at full resolution and the rest at coarser octree levels. Experiments show that SpaRV keeps the geometry consistent away from the captured trajectory and sharpens textures by optimizing the camera parameters. On one 24\,GB GPU, SpaRV reconstructs a university campus captured in 63{,}582 images and simulates other cameras and LiDARs. Our code will be released at \url{https://github.com/hku-mars/SpaRV}.
