Information-Theoretic Active Probing for Deformable Linear Objects (DLOs)

Inspired by Sun et al.’s robust DLO pipeline (2024) and CUT3R’s “virtual probing” of unobserved views (Wang et al., 2025), this project defines an information-theoretic objective—topological uncertainty of the reconstructed curve—to drive active viewpoint selection and physical probing (e.g., robot-induced minimal tensioning for disambiguation). It integrates DUSt3R’s pose-free pointmap regression (Wang et al., 2023) for rapid updates, and formalizes error propagation with stereo baselines (cf. Zhang & Boult, 2011) adapted to flexible, self-occluding curves. DLO reconstruction is confounded by self-occlusions and ambiguous depth. Existing pipelines reconstruct passively; here, the approach seeks the next-best-observation and minimal-interaction actions that provably reduce topology errors (knots, crossings) under sensor and motion constraints. It reconfigures the problem from passive perception to active, formal optimization of “topological risk,” extending classical baseline/error models to non-rigid, filamentary objects. It complements Sun et al.’s perception with an action loop and uses CUT3R’s persistent state to fuse evidence over time. Accurate DLO models are crucial in robotic wiring, surgical suturing, and textile automation. A principled planner could cut failure rates in downstream manipulation by resolving ambiguities early with minimal extra sensing. Impact includes a general recipe for active 3D perception of thin, deformable structures, with formal guarantees on uncertainty reduction and demonstrable gains in manipulation success rates.

References:

1. A Robust Deformable Linear Object Perception Pipeline in 3D: From Segmentation to Reconstruction. Zhaole Sun, Hang Zhou, Li Nanbo, Longfei Chen, Jihong Zhu, Robert B. Fisher (2024). IEEE Robotics and Automation Letters.
2. Continuous 3D Perception Model with Persistent State. Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A. Efros, Angjoo Kanazawa (2025). Computer Vision and Pattern Recognition.
3. Realistic stereo error models and finite optimal stereo baselines. Zhang Tao, T. Boult (2011). 2011 IEEE Workshop on Applications of Computer Vision (WACV).
4. DUSt3R: Geometric 3D Vision Made Easy. Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, Jérôme Revaud (2023). Computer Vision and Pattern Recognition.