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Found 9 papers, 7 papers with code
Exploiting Sparsity in Automotive Radar Object Detection Networks
This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources.
Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks
The proposed multi-scale KPPillarsBEV architecture outperforms the baseline by 5. 37% and the previous state of the art by 2. 88% in Car AP4. 0 (average precision for a matching threshold of 4 meters) on the nuScenes validation set.
Lossless Coding of Point Cloud Geometry using a Deep Generative Model
This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy.
Multiscale deep context modeling for lossless point cloud geometry compression
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.
A deep perceptual metric for 3D point clouds
In addition, we propose a novel truncated distance field voxel grid representation and find that it leads to sparser latent spaces and loss functions that are more correlated with perceived visual quality compared to a binary representation.
Learning-based lossless compression of 3D point cloud geometry
On the one hand, octree representation can eliminate the sparsity in the point cloud.
Improved Deep Point Cloud Geometry Compression
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.
Folding-based compression of point cloud attributes
However, as this mapping process is lossy in nature, we propose several strategies to refine it so that attributes can be mapped to the 2D grid with minimal distortion.
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions.
