Neural Volume Rendering (NVR), a novel paradigm for the longstanding problem of photo-realistic rendering of virtual worlds, has developed explosively in the past three years. The unique and substantial computational requirements of NVR pose challenge on deploying NVR to existing dedicated accelerator for neural networks. In this work, we propose ZeroTetris, a spacial feature similarity-based sparse multilayer perceptron (MLP) hardware accelerator for NVR. By leveraging the unique similarity-based sparsity between adjacent sampling points in NVR models, ZeroTetris efficiently bypass the computation of zero activations, thereby enhancing energy efficiency. Evaluation results affirm the effectiveness of the proposed design, showcasing ZeroTetris's superior performance in both area and power efficiency compared to other dedicated sparse matrix multiplication or MLP accelerator designs.