We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit metric is binary, we focus on perceptual metrics that express the degree of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations are requested for batches of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to decorrelate batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion. Experiments indicate our method is general, adaptable, and outperforms the state-of-the-art.

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In the real world, we often come across soft objects having spatially varying stiffness, such as human palm or a wart on the skin. In this paper, we propose a novel approach to render thin, deformable objects having spatially varying stiffness (inhomogeneous material). We use the classical Kirchhoff thin plate theory to compute the deformation. In general, the physics-based rendering of an arbitrary 3D surface is complex and time-consuming. Therefore, we approximate the 3D surface locally by a 2D plane using an area-preserving mapping technique -Gall-Peters mapping. Once the deformation is computed by solving a fourthorder partial differential equation, we project the points back onto the original object for proper haptic rendering. The method was validated through user experiments and was found to be realistic.

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