Mar 2, 2023 · In this paper, we introduce two novel point cloud map consistency losses, which facilitate self-supervised learning on real data of lidar depth correction ...
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We introduce two novel point cloud map consistency losses, which facilitate self-supervised learning on real data of lidar depth correction models.
May 23, 2024 · In this paper, novel point cloud map consistency losses as well as depth measurement correction models were presented. To tackle the lack of ...
Jun 20, 2023 · In this letter, we introduce two novel point cloud map consistency losses, which facilitate self-supervised learning on real data of lidar depth ...
Two novel point cloud map consistency losses are introduced, which facilitate self-supervised learning on real data of lidar depth correction models in ...
May 23, 2024 · In this paper, we introduce two novel point cloud map consistency losses, which facilitate self-supervised learning on real data of lidar depth ...
Mar 1, 2023 · FEE Corridor. Introduced by Agishev et al. in Self-Supervised Depth Correction of Lidar Measurements from Map Consistency Loss. The data set ...
A multi-scale feature map synthesis loss for depth estimation is designed. An optical flow consistency loss for depth estimation is designed.
Missing: Correction Lidar Measurements
These sensors can provide accurate depth measurements and are robust to the change of lighting conditions, but their data are sparse. Therefore, visual and ...
Missing: Correction | Show results with:Correction
Self-supervised Visual-LiDAR Odometry with Flip Consistency
ieeexplore.ieee.org › abstract › document
In this paper, we incorporate sparse but accurate depth measurements obtained from lidars to overcome the limitation of visual methods. ... depth maps projected ...
Missing: Correction | Show results with:Correction