Reflective tomography LiDAR （RTL） reconstructs target contours by acquiring laser echo projection data， but incomplete angular detection in practice often leads to insufficient projection data. To address this issue， the authors propose a target contour reconstruction method that combines the structural sparsity of projection data with a super-resolution convolutional neural network （SRCNN）， based on the principles and technical implementation of RTL. This approach effectively resolves the failure of traditional algorithms when projection data suffers from severe angular deficiency. Different from conventional RTL imaging methods that directly incorporate sparse reconstruction models， the authors first recover full-angle projection data by integrating sparse constraints with SRCNN based on geometry prior of the projection data， followed by standard RTL imaging algorithms to achieve complete target contour reconstruction. To validate the effectiveness of the proposed method， the authors design laser echo projection simulations based on the facet model and conduct field experiments. The results demonstrate that the authors achieve high-quality target contour reconstruction under varying levels of projection data missing conditions.