DRHeC: Differentiable Rendering for Hand-Eye Calibration with RGB-Based Gradients
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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Recent advances in neural scene representations enable photorealistic novel-view synthesis, yet most methods remain tightly coupled to a single rendering paradigm, limiting their versatility and integration with conventional graphics workflows. We introduce Floating Radiance Networks (FlaRe), a neural scene representation combining explicit ray-traceable geometry with continuous neural radiance functions.
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Projector-camera (ProCams) systems achieve active scene perception and controllable appearance manipulation via structured illumination, serving as a core infrastructure for spatial augmented reality, projection mapping, and surface reflectance acquisition. Existing inverse-rendering methods for ProCams deliver high-fidelity results but rely on time-consuming per-scene optimization, while mainstream feed-forward 3D reconstruction models produce baked appearance that cannot adapt to spatially varying projector illumination.
Hydra introduces a marker‑free RGB‑D hand‑eye calibration method that leverages a novel ICP algorithm with a robust point‑to‑plane objective on a Lie algebra. Experiments on three serial manipulators and two RGB‑D cameras show that with only three random robot configurations the method achieves about 90% successful calibrations, 2–3× faster convergence to the global optimum, and 2 orders of magnitude faster convergence time (0.8 ± 0.4 s) compared to other marker‑free baselines. The approach delivers improved accuracy (5 mm in task space versus 7 mm for classical methods) while remaining marker‑free, and the authors provide an open‑source dataset, code, and ROS 2 integration.