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Dejan Knez

Publications and source records attributed to Dejan Knez.

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trueform: Fast And Robust Mesh CSG Via Topological Aggregation

Mesh CSG output is consumed in floating point: however exact the computation, every emitted coordinate is materialised -- rounded to a representable position -- and the next stage can observe crossings and orderings the exact result never had. Only index-based topology survives materialisation. We keep it exact: within the build, the arrangement's radial structure is ordered by exact predicates on the original input planes -- exact without exact constructions -- and where a decision spans faces, the intended answer is recovered by topological aggregation: a majority vote over the disagreeing geometric observations within their topological unit. We compute the arrangement locally with integer-exact predicates, every stage a graph problem on graphs it never explicitly constructs. Pairwise intersections are classified into five canonical types (VV, VE, VF, EE, EF), each cut face is arranged in its own plane, and a two-level identity keeps the result consistent across faces with no global structure. The arrangement and its domain partition are built once and queried arbitrarily often: a boolean of any arity is a per-domain bit test, volumetric regions read straight off the partition, and open surfaces -- declared as oriented sheets -- cut volumes through the same algebra. The method is implemented in the header-only trueform library, in C++ with Python and TypeScript bindings. Compared to prior art, it produces valid, watertight output while running up to two orders of magnitude faster, and stays interactive in the browser.

cs.CG

Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis

Anterior Vertebral Body Growth Modulation (AVBGM) is a minimally invasive surgical technique that gradually corrects spine deformities while preserving lumbar motion. However the selection of potential surgical patients is currently based on clinical judgment and would be facilitated by the identification of patients responding to AVBGM prior to surgery. We introduce a statistical framework for predicting the surgical outcomes following AVBGM in adolescents with idiopathic scoliosis. A discriminant manifold is first constructed to maximize the separation between responsive and non-responsive groups of patients treated with AVBGM for scoliosis. The model then uses subject-specific correction trajectories based on articulated transformations in order to map spine correction profiles to a group-average piecewise-geodesic path. Spine correction trajectories are described in a piecewise-geodesic fashion to account for varying times at follow-up exams, regressing the curve via a quadratic optimization process. To predict the evolution of correction, a baseline reconstruction is projected onto the manifold, from which a spatiotemporal regression model is built from parallel transport curves inferred from neighboring exemplars. The model was trained on 438 reconstructions and tested on 56 subjects using 3D spine reconstructions from follow-up exams, with the probabilistic framework yielding accurate results with differences of 2.1 +/- 0.6deg in main curve angulation, and generating models similar to biomechanical simulations.

cs.CV