Neural Radiance Fields (NeRF) have demonstrated remarkable capabilities in high-fidelity 3D scene reconstruction. However, existing methods face challenges in balancing computational efficiency with anti-aliasing performance. While TensoRF improves training efficiency by leveraging explicit voxel grids and tensor decomposition, it fails to handle multi-scale data, leading to aliasing artifacts. In contrast, Mip-NeRF addresses the anti-aliasing issue through cone sampling and integrated positional encodings but relies on an implicit MLP-based representation, which is incompatible with explicit voxel grids. To solve these problems, this paper proposed RC-NeRF, a NeRF framework that incorporates a new sampling strategy combining both cone and ray sampling within explicit voxel grids, to achieve a balance between efficiency and robust anti-aliasing. RC-NeRF introduces learnable weights to adaptively associate the outputs of cone and ray sampling, and incorporates a regularization term to prevent the model from converging to a local optimum. The experimental results show that the proposed RC-NeRF not only improves performance in multi-scale view synthesis but also suppresses floating artifacts introduced by input real-world images.