# NIDAR：NIR引导的本征分解用于可扩展的场景无关LiDAR强度重建

> 原文链接：https://robotics.hellobike.com/papers/2026-09-29-2609-36878
> 频道：papers | 更新日期：2026-09-29 | 标签：arxiv, cs.RO
> 核心摘要：NIDAR用伪NIR、本征分解与几何调制，从RGB和仿真几何合成LiDAR强度，并在Waymo、nuScenes及仿真SLAM中验证。

# NIDAR：NIR引导的本征分解用于可扩展的场景无关LiDAR强度重建

::: v-pre
> 原文链接：[NIDAR: NIR-Guided Intrinsic Decomposition for Scalable Scene-Agnostic LiDAR Intensity Reconstruction](https://arxiv.org/abs/2609.36878)（arXiv 2609.36878 · 发布日期：2026-09-29）

**NIDAR: NIR-Guided Intrinsic Decomposition for Scalable Scene-Agnostic LiDAR Intensity Reconstruction**

NIDAR用伪NIR、本征分解与几何调制，从RGB和仿真几何合成LiDAR强度，并在Waymo、nuScenes及仿真SLAM中验证。

## 核心要点

- 动机是LiDAR强度有助于感知与状态估计，但仿真常缺失或依赖真实强度监督和逐场景优化。
- 方法以馈送前向框架结合伪NIR翻译、分层本征分解、几何感知调制和源域分布校准。
- 学习组件用Waymo离线训练，评估时在Waymo和nuScenes上固定权重与校准，无需目标场景强度标签或拟合。
- 结果显示相较所评基线具竞争性像素精度和较好结构、感知保真度，并已接入UE5、Isaac Sim和生成式LiDAR流程。

**验证范围与局限**：下游验证仅为两个仿真室内场景中的强度感知SLAM，未构成真实机器人、真实传感器等验证。

## 英文摘要 (Abstract)

LiDAR return intensity provides complementary surface-response cues for robotic perception and state estimation, yet many simulation pipelines omit it or reproduce it using reconstruction methods that require real intensity supervision and per-scene optimization. These requirements increase data-collection and fitting costs and limit reuse across simulated scenes. We present NIDAR, a feed-forward framework that synthesizes dense intensity-like observations from RGB appearance and simulator geometry. NIDAR combines pretrained pseudo-NIR translation, hierarchical intrinsic decomposition, geometry-aware modulation, and source-domain distribution calibration to transfer reflectance-related image cues to simulated point clouds. Its learned components are trained offline using Waymo data; their weights and calibration remain fixed during evaluation on Waymo and nuScenes. Deployment therefore requires neither target-scene intensity labels nor target-scene gradient-based fitting. The reported comparisons show competitive pixel-wise accuracy and favorable structural and perceptual fidelity against the evaluated reconstruction baselines. A controlled pseudo-NIR-versus-RGB diagnostic further shows that the pseudo-NIR prior is most beneficial when used through the paper-aligned reflectance-and-remapping route, rather than as a simple direct intensity regressor. We further integrate NIDAR with Unreal Engine 5, Isaac Sim, and a generative LiDAR pipeline. Two intensity-aware SLAM systems evaluated in two simulated indoor scenes suggest potential downstream utility, but do not constitute real-robot validation. NIDAR therefore offers a scalable intensity-synthesis interface for the evaluated settings; cross-wavelength, camera-configuration, embedded, and real-sensor validation remain future work.

作者：Junjie Zhang, Jie Yin, Kefei Qian, Jie Li, Mengpei Jia, Yajuan Dun, Wenbo Chu, Guofa Li
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## 原始参考来源 (Sources)

- [arXiv 2609.36878：NIDAR: NIR-Guided Intrinsic Decomposition for Scalable Scene-Agnostic LiDAR Intensity Reconstruction](https://arxiv.org/abs/2609.36878)