P Singhal, R Walambe, S Ramanna, K Kotecha - IEEE access, 2023 - ieeexplore.ieee.org
Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well on another set of data (target domain), which is different but has similar properties as the …
Semantic occupancy perception is essential for autonomous driving, as automated vehicles require a fine-grained perception of the 3D urban structures. However, existing relevant …
Training deep models for semantic scene completion is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inherent label …
LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep …
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many …
An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly …
A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the …
We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which …
Abstract Knowledge transfer from synthetic to real data has been widely studied to mitigate data annotation constraints in various computer vision tasks such as semantic segmentation …