AdaptiveSplat: Texture Aware Controllable 3D Gaussian Allocation for Feed-Forward Reconstruction

Current feed-forward 3D reconstruction methods predict pixelaligned Gaussian primitives, resulting in highly redundant representa-tions. A natural solution is to prune the redundant Gaussians, but naivepruning introduces severe artifacts and often requires inference time fine-tuning, breaking the feed-forward paradigm. Based on previous works,high frequency regions require more Gaussian primitives, while low fre-quency regions can be represented with significantly fewer primitives.Motivated by this, we propose a novel approach to explicitly controlthe number of Gaussians by leveraging local texture information. Ourapproach achieves this through three key components: (1) texture es-timation to capture spatial variation in scene detail, (2) texture-awarepruning that removes redundant Gaussians from low frequency regions,and (3) an adaptive Gaussian head that predicts the modified attributesof the retained primitives without breaking the feed-forward paradigm.Experiments on RE10K, ACID, DL3DV, Tanks and Temples, and DTUdemonstrate the effectiveness of our approach, while ablation studiesvalidate the contributions of its key components.