Chart corpora, which comprise data visualizations and their semantic labels, are crucial for advancing visualization research. However, the labels in most existing corpora are high-level (e.g., chart types), hindering their utility for broader applications in the era of AI. In this paper, we contribute VisAnatomy, a corpus containing 942 real-world SVG charts produced by over 50 tools, encompassing 40 chart types and featuring structural and stylistic design variations. Each chart is augmented with multi-level fine-grained labels on its semantic components, including each graphical element's type, role, and position, hierarchical groupings of elements, group layouts, and visual encodings. In total, VisAnatomy provides labels for more than 383k graphical elements. We demonstrate the richness of the semantic labels by comparing VisAnatomy with existing corpora. We illustrate its usefulness through four applications: semantic role inference for SVG elements, chart semantic decomposition, chart type classification, and content navigation for accessibility. Finally, we discuss research opportunities to further improve VisAnatomy.

Reinforcement learning with general utilities (RLGU) offers a unifying framework to capture several problems beyond standard expected returns, including imitation learning, pure exploration, and safe RL. Despite recent fundamental advances in the theoretical analysis of policy gradient (PG) methods for standard RL and recent efforts in RLGU, the understanding of these PG algorithms and their scope of application in RLGU still remain limited. In this work, we establish global optimality guarantees of PG methods for RLGU in which the objective is a general concave utility function of the state-action occupancy measure. In the tabular setting, we provide global optimality results using a new proof technique building on recent theoretical developments on the convergence of PG methods for standard RL using gradient domination. Our proof technique opens avenues for analyzing policy parameterizations beyond the direct policy parameterization for RLGU. In addition, we provide global optimality results for large state-action space settings beyond prior work which has mostly focused on the tabular setting. In this large scale setting, we adapt PG methods by approximating occupancy measures within a function approximation class using maximum likelihood estimation. Our sample complexity only scales with the dimension induced by our approximation class instead of the size of the state-action space.

We present a novel light field structure-from-motion (SfM) framework for reliable 3D object reconstruction. Specifically, we use the light field (LF) camera such as Lytro and Raytrix as a virtual 3D scanner. We move an LF camera around the object and register between multiple LF shots. We show that applying conventional SfM on sub-aperture images is not only expensive but also unreliable due to ultra-small baseline and low image resolution. Instead, our LF-SfM scheme maps ray manifolds across LFs. Specifically, we show how rays passing through a common 3D point transform between two LFs and we develop reliable technique for extracting extrinsic parameters from this ray transform. Next, we apply a new edge-preserving stereo matching technique on individual LFs and conduct LF bundle adjustment to jointly optimize pose and geometry. Comprehensive experiments show our solution outperforms many state-of-the-art passive and even active techniques especially on topologically complex objects.

Traditional Structure-from-Motion (SfM) uses images captured by cameras as inputs. In this paper, we explore using light fields captured by plenoptic cameras or camera arrays as inputs. We call this solution plenoptic SfM or P-SfM solution. We first present a comprehensive theory on ray geometry transforms under light field pose variations. We derive the transforms of three typical ray manifolds: rays passing through a point or point-ray manifold, rays passing through a 3D line or ray-line manifold, and rays lying on a common 3D plane or ray-plane manifold. We show that by matching these manifolds across LFs, we can recover light field poses and conduct bundle adjustment in ray space. We validate our theory and framework on synthetic and real data on light fields of different scales: small scale LFs acquired using a LF camera and large scale LFs by a camera array. We show that our P-SfM technique can significantly improve the accuracy and reliability over regular SfM and PnP especially on traditionally challenging scenes where reliable feature point correspondences are difficult to obtain but line or plane correspondences are readily accessible.