arXiv:2610.33500v1 Announce Type: new Abstract: In future integrated sensing and communication (ISAC) systems, distributed multiple-input multiple-output (MIMO) architectures offer high-resolution imaging capabilities. However, reconstructing real-world extended targets is physically challenging due to continuous anisotropic surface scattering and complex occlusions, especially in the presence of system synchronization errors. To overcome these limitations, this paper proposes a hierarchical two-stage computational imaging framework for extended target reconstruction. In Phase I, an incoherent coarse imaging process is performed to serve as a reliable initializer for subsequent high-resolution coherent image generation. By performing line-of-sight (LOS) interference suppression and leveraging network-wide topological constraints, timing and phase offsets are calibrated. This enables image construction by back-projection (BP) and the coarse identification of target contours using the probabilistic Hough transform (PHT). In Phase II, a joint target position and orientation off-grid compensation model is established, capturing Lambertian diffuse and specular reflections on continuous facets via a first-order Taylor expansion. To tackle severe occlusion and spatial grid mismatches, we propose the bilinear off-grid approximate message passing (BiOG-AMP) algorithm. BiOG-AMP iteratively updates surface visibility and dictionary matrices, enforcing physical consistency across distributed transceiver views. Theoretical analysis derives compressed sensing error bounds, Fisher information limits, and linearization error scaling. Simulation results demonstrate that the proposed framework effectively eliminates imaging artifacts and exhibits superior robustness against synchronization offsets and grid mismatches compared to state-of-the-art baselines.