Abstract
Optimal camera placement is fundamental for inspection and 3D reconstruction tasks. We address this traditionally discrete problem by reformulating it into a continuous optimization task using Differentiable Rendering (DR), a technique that enables gradient computation from 3D camera poses to a final loss value. Assuming a known scene, our pipeline treats camera poses as learnable parameters. We implement a novel differentiable visibility formulation that compares observed depths from the rendered scene, with the expected depth of the mesh vertices. Based on this, we define a geometry-based loss function using two specific coverage criteria: ”At-Least-Once” (ALO) and ”Exactly-Once” (EO), which find optimal placement configurations. Experiments conducted on diverse 3D objects and camera configurations demonstrate that our approach outperforms the SOTA baseline Neural Observation Field Guided Hybrid Optimization of Camera Placement (NeOF). Our methods yield superior quality, as demonstrated by our quantitative evaluation, and achieve lower computation times. Furthermore, the quality and efficiency of the generated poses show strong potential for future integration into trajectory planning systems.
Ane Moraza
PhD Student and Research Assistant
My research focuses on bridging the sim-to-real gap in industrial environments using Differentiable Rendering and Deep Learning techniques.
Nagore Barrena
Assistant Professor
My research focuses on computer vision and deep learning, with applications to biomedical and natural images.