Bounded Appearance Field for In-the-Wild 3D Gaussian Splatting

In unconstrained photo collections, appearance variations and transient occluders violate multi-view consistency, a key assumption of 3D Gaussian Splatting (3DGS). Methods with per-Gaussian appearance features tie spatial appearance capacity to geometric densification, although finer geometry need not require more complex regional appearance. We introduce a Bounded Appearance Field (BAF) that combines a shared-field of fixed capacity with global color adjustments and per-Gaussian local corrections. A fixed-resolution token grid is mixed once per image and queried by Gaussians through trilinear interpolation. This keeps shared field capacity independent of the Gaussian count, while local storage and per-Gaussian computation remain linear in that count. To support adaptation from partial observations of a held-out view, we project color residuals and coverage into the grid, providing spatially aligned appearance evidence for predictions outside the observed region. We additionally use frozen DINOv3 feature distances to supervise a heteroscedastic 2D uncertainty head. Its predicted weights modulate the photometric loss and the gradients driving densification to reduce the influence of transient observations. On both Photo Tourism and NeRF On-the-go datasets, our method converges within 30k updates and matches or surpasses state-of-the-art results. Our code will be available after acceptance.