Sparse Voxels Rasterization introduces a novel approach to real-time high-fidelity rendering of radiance fields, tackling the challenges of recovering voxel representations from a limited set of 2D images. This method avoids the need for dense optical flow and claims to solve an inverse problem effectively, making it distinguishable from traditional techniques like NeRF. The user comments reflect enthusiasm about the implications of the technique, drawing parallels to foundational works in the field, and emphasize its potential utility in rendering novel views from sparse input data. Key inquiries also touch on the specifics of the necessary input data—highlighting the necessity for clarity on whether still images or videos can be used and what quantity is optimal for effective rendering. The ongoing interest in understanding these variables showcases a broader need for clarity and implementation guidelines within the community of users engaged in radiance field applications.