BADGR: Bundle Adjustment Diffusion Conditioned by Gradients for Wide-Baseline Floor Plan Reconstruction

Reconstructing precise camera poses and floor plan layouts from a set of wide-baseline RGB panoramas is a difficult and unsolved problem. We present BADGR, a novel diffusion model which performs both reconstruction and bundle adjustment (BA) optimization tasks, to refine camera poses and layouts from a given coarse state using 1D floor boundary information from dozens of images of varying input densities. Unlike a guided diffusion model, BADGR is conditioned on dense per-feature outputs from a single-step Levenberg-Marquardt (LM) optimizer and is trained to predict camera and wall positions while minimizing reprojection errors for view-consistency. The objective of layout generation from denoising diffusion process complements BA optimization by providing additional learned layout-structural constraints on top of the co-visible features across images. These constraints help BADGR to make plausible guesses on spatial relations which help constrain pose graph, such as wall adjacency, collinearity, and learn to mitigate errors from dense boundary observations with global contexts. BADGR trains exclusively on 2D floor plans, simplifying data acquisition, enabling robust augmentation, and supporting variety of input densities. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art pose and floor plan layouts reconstruction with different input densities.