Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Quach, Maurice"'
Autor:
Armanious, Karim, Quach, Maurice, Ulrich, Michael, Winterling, Timo, Friesen, Johannes, Braun, Sascha, Jenet, Daniel, Feldman, Yuri, Kosman, Eitan, Rapp, Philipp, Fischer, Volker, Sons, Marc, Kohns, Lukas, Eckstein, Daniel, Egbert, Daniela, Letsch, Simone, Voege, Corinna, Huttner, Felix, Bartler, Alexander, Maiwald, Robert, Lin, Yancong, Rüegg, Ulf, Gläser, Claudius, Bischoff, Bastian, Freess, Jascha, Haug, Karsten, Klee, Kathrin, Caesar, Holger
This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. Unlike existing datasets, BSD offers a unique inte
Externí odkaz:
http://arxiv.org/abs/2407.12803
Autor:
Lippke, Marius, Quach, Maurice, Braun, Sascha, Köhler, Daniel, Ulrich, Michael, Bischoff, Bastian, Tan, Wei Yap
Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fundamental part of such systems. CNN-based object detectors showed good p
Externí odkaz:
http://arxiv.org/abs/2308.07748
Autor:
Köhler, Daniel, Quach, Maurice, Ulrich, Michael, Meinl, Frank, Bischoff, Bastian, Blume, Holger
Architectures that first convert point clouds to a grid representation and then apply convolutional neural networks achieve good performance for radar-based object detection. However, the transfer from irregular point cloud data to a dense grid struc
Externí odkaz:
http://arxiv.org/abs/2305.15836
Autor:
Ak, Ali, Zerman, Emin, Quach, Maurice, Chetouani, Aladine, Smolic, Aljosa, Valenzise, Giuseppe, Callet, Patrick Le
Publikováno v:
IEEE Transactions on Multimedia vol. 26 (2024) pp. 6730-6742
Point clouds have become increasingly prevalent in representing 3D scenes within virtual environments, alongside 3D meshes. Their ease of capture has facilitated a wide array of applications on mobile devices, from smartphones to autonomous vehicles.
Externí odkaz:
http://arxiv.org/abs/2302.04796
This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy. First, to take into account the PC sparsity, our method adaptively partitions a point c
Externí odkaz:
http://arxiv.org/abs/2107.00400
We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec. Our previous work based on autoregressive models (Voxe
Externí odkaz:
http://arxiv.org/abs/2104.09859
Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recently, point cloud geometry compression approaches based on deep neural
Externí odkaz:
http://arxiv.org/abs/2102.12839
This paper presents a learning-based, lossless compression method for static point cloud geometry, based on context-adaptive arithmetic coding. Unlike most existing methods working in the octree domain, our encoder operates in a hybrid mode, mixing o
Externí odkaz:
http://arxiv.org/abs/2011.14700
Point clouds have been recognized as a crucial data structure for 3D content and are essential in a number of applications such as virtual and mixed reality, autonomous driving, cultural heritage, etc. In this paper, we propose a set of contributions
Externí odkaz:
http://arxiv.org/abs/2006.09043
Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent advances in point cloud representation learning. Point clouds can be inter
Externí odkaz:
http://arxiv.org/abs/2002.04439