Uncertainty Neural Surfaces for Space Target 3D Reconstruction Under Constrained Views

In asteroid exploration and orbital servicing missions with space robots, accurate 3D structural of the target is typically relied upon for planning landing trajectories and controlling movements. Unlike conventional neural radiance fields (NeRF) studies, which rely on full-view random sampling of targets that can be easily achieved on the ground, spacecraft operations present unique challenges due to the kinematic orbit constraint, the high cost of controlled motion, and limited fuel reserves. This results in limited observation of space targets. In order to obtain 3D structure under close-flybys and restricted observation, we proposed Uncertainty Neural Surfaces (UNS) model based on Bayesian uncertainty estimation. UNS enhance the precision of reconstructed target surfaces under constrained-views, providing guidance for subsequent imaging view design. Specifically, UNS introduces Bayesian estimation based surface uncertainty on neural implicit surfaces. The estimation is calculated based on the degree of self-occlusion of the target and the difference between rendered and actual colors. This approach enables uncertain estimation of 3D space and arbitrary view. Finally, extensive systematic evaluations and analyses of spacecraft model sampling in a local darkroom validate the sophistication of UNS in uncertainty estimation and surface reconstruction quality. Code is available at https://github.com/YD-96/UNS.