10 accepted papers
Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera can be deployable at a low cost and compact size. Due to its significant and growing needs, many lightweight monocular dep…
Is it possible for a synthetic to realistic domain adapted neural network in single image depth estimation to truly generalize on real world data? The resultant, adapted model will only generalize on the realistic domain dataset, which only reflects a small portion of the true, real world. As a resu…
Despite its importance, unsupervised domain adaptation (UDA) on LiDAR semantic segmentation is a task that has not received much attention from the research community. Only recently, a completion-based 3 DD method has been proposed to tackle the problem and formally set up the adaptive scenarios. Ho…
In an attempt to imitate the success of transformers in the field of natural language processing into computer vision tasks, vision transformers (ViTs) have recently gained attention. Performance breakthroughs have been achieved in coarse-grained tasks like classification. However, dense prediction…
The depth completion task aims to predict a dense depth map from a sparse LiDAR point cloud and an RGB image. This task is critical because an accurate depth map can be used as prior information to solve many computer vision tasks, such as downstream tasks in autonomous vehicles and robot vision. Pr…
"Modern deep neural networks are equipped with normalization layers such as batch normalization or layer normalization to enhance and stabilize training dynamics. If a network contains such normalization layers, the optimization objective is invariant to the scale of the neural network parameters. T…
Autonomous agents need to perceive the world in a robust way, such that the shift in data distribution does not lead to faulty perception results. When agents cannot be trained with abundant data, agents may need to operate on real world environments while trained on simulated data, and suffer from…
Convolutional neural networks (CNNs) have achieved remarkable results; however, despite the development of deep learning, practical user applications are fairly limited because heavy networks can be used solely with the latest hardware and software supports. Therefore, network pruning is gaining att…
In computer vision, monocular depth estimation is the problem of obtaining a high-quality depth map from a two-dimensional image. This map provides information on three-dimensional scene geometry, which is necessary for various applications in academia and industry, such as robotics and autonomous d…
Following considerable development in 3D scanning technologies, many studies have recently been proposed with various approaches for 3D vision tasks, including some methods that utilize 2D convolutional neural networks (CNNs). However, even though 2D CNNs have achieved high performance in many 2D vi…