  Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream
methodologies for novel view synthesis (NVS) due to its high quality and fast
rendering speed. However, as a point-based scene representation, 3DGS
potentially generates a large number of Gaussians to fit the scene, leading to
high memory usage. Improvements that have been proposed require either an
empirical and preset pruning ratio or importance score threshold to prune the
point cloud. Such hyperparamter requires multiple rounds of training to
optimize and achieve the maximum pruning ratio, while maintaining the rendering
quality for each scene. In this work, we propose learning-to-prune 3DGS
(LP-3DGS), where a trainable binary mask is applied to the importance score
that can find optimal pruning ratio automatically. Instead of using the
traditional straight-through estimator (STE) method to approximate the binary
mask gradient, we redesign the masking function to leverage the Gumbel-Sigmoid
method, making it differentiable and compatible with the existing training
process of 3DGS. Extensive experiments have shown that LP-3DGS consistently
produces a good balance that is both efficient and high quality.
