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Can a feed-forward 3D reconstruction model discard most of its Gaussians and still preserve scene detail? This episode explains how BudSplat makes primitive allocation part of reconstruction, from visual-token importance estimation through voxel fusion and pruning. We cover three findings: roughly twentyfold fewer Gaussians while matching AnySplat's PSNR in the largest RealEstate10K setting; why removing the detail-estimation branch causes the strongest ablation degradation; and how local quotas distribute a fixed representation budget across image regions. BudSplat builds on an AnySplat-style pipeline with a VGGT geometry backbone, trains on RealEstate10K and DL3DV, and evaluates novel-view synthesis on captured imagery, including held-out ACID scenes. We connect its design to LightGaussian's compression pipeline and RLSplat's direct budgeted generation approach. The central limitation is that controlling Gaussian count does not establish an end-to-end runtime or memory budget for onboard robotics; dense geometry processing still precedes pruning. For researchers working on compact visual maps, embodied perception, or reusable scene assets, the episode identifies the architectural idea worth borrowing. What should a reconstruction network protect before it starts throwing information away?
