Despite significant advancements in single-view intrinsic image decomposition, a domain disparity exists due to the limited information that can be obtained from a single-view image and the ill-posed nature of the problem of intrinsic image decomposition. Multi-view images offer an alternative method to circumvent the ambiguity present in 2D intrinsic image decomposition. Building on the concepts of multi-view intrinsic images and recent neural rendering techniques, we propose Intrinsic-GS, a multiview intrinsic image decomposition method utilizing Gaussian-splatting. To achieve this, we first augment each Gaussian ellipsoid with additional attributes (i.e., albedo, shading, and a residual term) to model the intrinsic radiance field. Next, we use several color-invariants and physics-based priors to jointly regularize the optimization of the intrinsic and composited radiance fields. Finally, we conduct experiments on both synthetic and real-world datasets, demonstrating stable intrinsic decomposition results across various (including non-Lambertian) objects and scenes.