Stealthy Coverage Control for Human-enabled Real-Time 3D Reconstruction

This is a paper-only artifact (0 stars, 0 forks, 0 velocity) with no public implementation or adoption. The core contribution—combining human-guided control with adaptive coverage sampling for 3D reconstruction—is a reasonable algorithmic novelty (combining known techniques: active vision, human-in-the-loop systems, complexity estimation), but the work lacks any production signal or community traction. The research addresses a genuine problem (optimal viewpoint planning under uncertainty), but: (1) no code repository is apparent, making this purely theoretical/reference; (2) frontier labs (Google, OpenAI, Anthropic via robotics partners, or Meta's research division) are actively working on 3D reconstruction, SLAM, and human-robot collaboration; (3) the capability—adaptive image sampling for 3D tasks—is easily subsumable as a feature within larger reconstruction pipelines or robotic systems; (4) without an implementation, reproducibility and defensibility are nil. The 'human-enabled' aspect adds a small moat (domain-specific interaction design), but not enough to offset the lack of embodied work, code, or users. Frontier risk is HIGH because this directly competes with active research in 3D perception at major labs, and the semi-autonomous framing is well-aligned with ongoing robotics + vision work.