Sparse coding (SC) is making a significant impact in computer vision and signal processing communities, which achieves the state-of-the-art performance in a variety of applications for images, e.g., denoising, restoration, and synthesis. We propose an adaptive and robust SC algorithm exploiting the characteristics of typical laser range data and the availability of both range and reflectance data to realize range data denoising and inpainting. Specifically, our method estimates the informative level of each patch according to the variation in both range and reflectance modalities, followed by adaptive dictionary training that assigns dynamic sparsity weights to the patches with different informative levels. Furthermore, the $\ell _{1}$ -norm-based representation fidelity measure is applied to make our method robust to outliers which are common in laser range measurements. Extensive experiments on synthetic and real data demonstrate that our method works effectively, resulting in superior performance both visually and quantitatively, compared with competitive methods including the available sparse-representation-based algorithm, wavelets, partial differential equation, and non-local means.