Details

Autor(en) / Beteiligte

• Piasco, Nathan
• Sidibe, Desire
• Demonceaux, Cedric
• Gouet-Brunet, Valerie

Titel

Geometric Camera Pose Refinement with Learned Depth Maps

Ist Teil von

• 2019 IEEE International Conference on Image Processing (ICIP), 2019, p.2561-2565

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• We present a new method for image-only camera relocalisation composed of a fast image indexing retrieval step followed by pose refinement based on ICP (Iterative Closest Point). The first step aims to find an initial pose for the query by evaluating images similarity with low dimensional global deep descriptors. Subsequently, we predict with a fully convolutional deep encoder-decoder neural network a dense depth map from the image query. We use this depth map to create a local point cloud and refine the initial query pose using an ICP algorithm.We demonstrate the effectiveness of our new approach on various indoor scenes. Compared to learned pose regression methods, our proposal can be used on multiple scenes without the need of a specific weights-setup for each scene, while showing equivalent results.