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CEA

Academic institutioneurope · fr
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Representative Papers

BOP-Distrib: Revisiting 6D Pose Estimation Benchmarks for Better Evaluation under Visual Ambiguities

Aug 30, 2024

Current 6D pose estimation benchmarks oversimplify visual ambiguities—such as symmetry and occlusion—as global object symmetries, neglecting image-level, viewpoint-dependent visibility variations, thereby misrepresenting real-world pose uncertainty. To address this, we propose a novel image-level pose distribution evaluation paradigm: (1) the first automatic pose distribution annotation method grounded in single-image surface visibility; (2) BOP-Dist, the first pose distribution benchmark tailored to realistic images; and (3) a symmetry-aware sampling strategy coupled with a distribution-aware accuracy/recall evaluation framework. After re-annotating all BOP datasets with pose distributions, we observe substantial corrections to the performance ranking of state-of-the-art single-solution methods—revealing their rankings to be highly sensitive to annotation granularity. This work establishes a physically interpretable, reproducible, and quantitative evaluation standard for multi-solution pose estimation.

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Size-varying reversible causal graph dynamics

May 25, 2018

Conventional wisdom holds that reversible graph dynamics must preserve the number of nodes, precluding node creation or deletion while maintaining reversibility. Method: This paper challenges this paradigm by introducing three mutually equivalent relaxed frameworks—grounded in reversible computation, extended cellular automata, and bijective graph rewriting—that jointly enforce global bijectivity and local causality while permitting reversible node creation and destruction. Contribution/Results: We formally prove the equivalence of these frameworks, thereby establishing the first causal graph dynamics model that is both size-variable and time-reversible. This work refutes the long-standing assumption that reversibility necessitates node conservation, offering a novel paradigm for discrete spacetime modeling. It bridges a critical gap between theoretical computer science—particularly models of reversible computation—and formal approaches to quantum gravity, where dynamical causal structure and background independence are essential.

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