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For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …
require a deeper understanding of the world in which they operate. In robotics and related …
Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark
Monocular depth estimation aims at predicting depth from a single image or video. Recently,
self-supervised methods draw much attention since they are free of depth annotations and …
self-supervised methods draw much attention since they are free of depth annotations and …
Self-supervised monocular depth estimation for all day images using domain separation
Remarkable results have been achieved by DCNN based self-supervised depth estimation
approaches. However, most of these approaches can only handle either day-time or night …
approaches. However, most of these approaches can only handle either day-time or night …
Steps: Joint self-supervised nighttime image enhancement and depth estimation
Self-supervised depth estimation draws a lot of attention recently as it can promote the 3D
sensing capa-bilities of self-driving vehicles. However, it intrinsically relies upon the …
sensing capa-bilities of self-driving vehicles. However, it intrinsically relies upon the …
Boosting object detection with zero-shot day-night domain adaptation
Detecting objects in low-light scenarios presents a persistent challenge as detectors trained
on well-lit data exhibit significant performance degradation on low-light data due to low …
on well-lit data exhibit significant performance degradation on low-light data due to low …
The third monocular depth estimation challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation
Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging …
Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging …
Unsupervised monocular depth estimation in highly complex environments
With the development of computational intelligence algorithms, unsupervised monocular
depth and pose estimation framework, which is driven by warped photometric consistency …
depth and pose estimation framework, which is driven by warped photometric consistency …
Self-supervised monocular depth estimation: Solving the edge-fattening problem
Self-supervised monocular depth estimation (MDE) models universally suffer from the
notorious edge-fattening issue. Triplet loss, popular for metric learning, has made a great …
notorious edge-fattening issue. Triplet loss, popular for metric learning, has made a great …