Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …
order to achieve robust and accurate scene understanding, autonomous vehicles are …
A review and comparative study on probabilistic object detection in autonomous driving
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …
recent years, deep learning has become the de-facto approach for object detection, and …
Aspanformer: Detector-free image matching with adaptive span transformer
Generating robust and reliable correspondences across images is a fundamental task for a
diversity of applications. To capture context at both global and local granularity, we propose …
diversity of applications. To capture context at both global and local granularity, we propose …
Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds
T Cortinhal, G Tzelepis, E Erdal Aksoy - … , ISVC 2020, San Diego, CA, USA …, 2020 - Springer
In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a
full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet 1 which has …
full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet 1 which has …
Freihand: A dataset for markerless capture of hand pose and shape from single rgb images
Estimating 3D hand pose from single RGB images is a highly ambiguous problem that relies
on an unbiased training dataset. In this paper, we analyze cross-dataset generalization …
on an unbiased training dataset. In this paper, we analyze cross-dataset generalization …
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Abstract Neural Radiance Fields (NeRFs) have shown promise in applications like view
synthesis and depth estimation but learning from multiview images faces inherent …
synthesis and depth estimation but learning from multiview images faces inherent …
Scale-space flow for end-to-end optimized video compression
Despite considerable progress on end-to-end optimized deep networks for image
compression, video coding remains a challenging task. Recently proposed methods for …
compression, video coding remains a challenging task. Recently proposed methods for …
Separable flow: Learning motion cost volumes for optical flow estimation
F Zhang, OJ Woodford… - Proceedings of the …, 2021 - openaccess.thecvf.com
Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
However, constructed using simple feature correlations, they lack the ability to encapsulate …
However, constructed using simple feature correlations, they lack the ability to encapsulate …
Computer vision for autonomous vehicles: Problems, datasets and state of the art
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
Image-to-image regression with distribution-free uncertainty quantification and applications in imaging
Image-to-image regression is an important learning task, used frequently in biological
imaging. Current algorithms, however, do not generally offer statistical guarantees that …
imaging. Current algorithms, however, do not generally offer statistical guarantees that …