In this episode we discuss gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction
by Zerui Chen, Shizhe Chen, Cordelia Schmid, Ivan Laptev. The paper presents a method for reconstructing 3D shapes of hands and manipulated objects from monocular RGB images using signed distance functions (SDFs) as a framework. The authors exploit the hand structure to guide the SDF-based shape reconstruction by estimating poses of hands and objects and aligning SDFs with highly-articulated hand poses. They also use temporal information to enhance the method's robustness to occlusion and motion blurs. Extensive experiments on challenging benchmarks demonstrate significant improvements over the state-of-the-art in 3D shape reconstruction.
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