SGOP3D: A Semantic–Geometric Anchoring and Observation-Preserving Reconstruction Algorithm for Multi-Object Robotic Manipulation

Multi-object 3D reconstruction for robotic manipulation requires consistent object identity across target admission, measured-surface fusion, and missing-geometry recovery, together with explicit rules governing the use of geometry from different sources in manipulation. We propose SGOP3D (Semantic–Geometric Anchoring and Observation-Preserving 3D Reconstruction), a semantically anchored algorithm for observation-preserving 3D reconstruction from two orthogonal views. The method combines our semantic and geometric detection networks, using class consistency, bidirectional top–front reprojection, and world-coordinate constraints to establish trusted object-level anchors. The same anchor governs measured-surface extraction and completion with our GenCompletionNet, converting complete-shape predictions into missing-geometry residuals that satisfy surface-protection and dual-view free-space constraints. Per-point provenance labels restrict contact localization to measured-source points and limit generated geometry to shape estimation and occupancy refinement. In a system-level comparison on 205 synchronized top–front observation pairs involving 20 objects arranged in two groups of 10, SGOP3D exceeds AMB3R by 36.45 and 21.10 percentage points in strict instance separation success and F@$10\mathrm{mm}$, respectively, under a shared scoring protocol. Across 400 controlled grasp–lift–hold trials with fixed detection, planning, and control, SGOP3D achieves 96% success versus 79% for the strongest geometric-input control. A separate continuous sorting demonstration with dynamic replenishment reports 19/20 first-attempt completions and 20/21 transfer-and-release completions. These results show that SGOP3D maintains object-consistency and observational-evidence constraints throughout geometric recovery and manipulation, providing object-level 3D geometry with traceable provenance for contact planning, grasping, and continuous sorting in multi-object robotic scenes.