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NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. ...Conference Paper -
Sat2Vid: Street-View Panoramic Video Synthesis From a Single Satellite Image
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We present a novel method for synthesizing both temporally and geometrically consistent street-view panoramic video from a single satellite image and camera trajectory. Existing cross-view synthesis approaches focus on images, while video synthesis in such a case has not yet received enough attention. For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense ...Conference Paper -
CompNVS: Novel View Synthesis with Scene Completion
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage. While generative neural approaches have demonstrated spectacular results on 2D images, they have not yet achieved similar photorealistic results in combination with scene completion where a spatial 3D scene understanding is essential. To this end, we propose a generative pipeline performing on a sparse grid-based neural ...Conference Paper -
Geometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present a novel method for generating panoramic street-view images which are geometrically consistent with a given satellite image. Different from existing approaches that completely rely on a deep learning architecture to generalize cross-view image distributions, our approach explicitly loops in the geometric configuration of the ground objects based on the satellite views, such that the produced ground view synthesis preserves the ...Conference Paper