Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Ding, Laiyan"'
Depth completion using lightweight time-of-flight (ToF) depth sensors is attractive due to their low cost. However, lightweight ToF sensors usually have a limited field of view (FOV) compared with cameras. Thus, only pixels in the zone area of the im
Externí odkaz:
http://arxiv.org/abs/2411.04480
Vision-based BEV (Bird-Eye-View) 3D object detection has recently become popular in autonomous driving. However, objects with a high similarity to the background from a camera perspective cannot be detected well by existing methods. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2410.10298
Depth estimation is a cornerstone for autonomous driving, yet acquiring per-pixel depth ground truth for supervised learning is challenging. Self-Supervised Surround Depth Estimation (SSSDE) from consecutive images offers an economical alternative. W
Externí odkaz:
http://arxiv.org/abs/2407.04041
Semantic Scene Completion (SSC) aims to jointly infer semantics and occupancies of 3D scenes. Truncated Signed Distance Function (TSDF), a 3D encoding of depth, has been a common input for SSC. Furthermore, RGB-TSDF fusion, seems promising since thes
Externí odkaz:
http://arxiv.org/abs/2403.16888
Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions. Fortunately, the indoor scenes are full of specific structures, such as planes and lines, which should help gu
Externí odkaz:
http://arxiv.org/abs/2110.05839
As a flexible passive 3D sensing means, unsupervised learning of depth from monocular videos is becoming an important research topic. It utilizes the photometric errors between the target view and the synthesized views from its adjacent source views
Externí odkaz:
http://arxiv.org/abs/2108.13062
3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using positively correlated high-level features. Curren
Externí odkaz:
http://arxiv.org/abs/2106.15413
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one. It achieves this by using the photometric errors between the targ
Externí odkaz:
http://arxiv.org/abs/2003.01360