Three terms (X,Y, Error ) are combined in a single floating- point number in an advanced microprocessor to compute pixel addresses. The floating-point number is reconverted to integer form in multiplexers operating in a mode determined by slope, relative to unity, of the line to be defined...
On-line Paper: [Paper] 1. Point cloud completion 1.1 Voxel-based methods [1] VConv-DAE: Deep Volumetric Shape Learning Without Object Labels (ECCV 2016) [Paper] [Code] [2] Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis (CVPR 2017) [Paper] [Code] ...
[arXiv] Fast 3D Line Segment Detection From Unorganized Point Cloud. [det.] [arXiv] Point-Cloud Saliency Maps. [tensorflow] [cls. oth.] [arXiv] Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers. [code] [oth.] [arxiv] Context Prediction for Unsupervised Deep Le...
Point cloud completion, the issue of estimating the complete geometry of objects from partially-scanned point cloud data, becomes a fundamental task in many 3d vision and robotics applications. To address the limitations on inadequate prediction of shape
Deformable attention only focuses on a small group of key sample-points around the reference point and make itself be able to capture dynamically the local features of input feature map without considering the size of the feature map. Its introduction in
theimage plane. If you trace a line from P to the eye (the origin of the camera coordinate system), P' is where this line intersects the canvas (as shown in Figure 7). When the coordinates of point P are defined with respect to the camera coordinate system, com...
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Do not begin your story with a first person narrator and then switch to a third person narrator. Do not start with third person limited and then abruptly give your narrator full omniscience. This is the most common type of error I see writers make with POV. The guideline I learned in my...
[arXiv] Fast 3D Line Segment Detection From Unorganized Point Cloud. [det.] [arXiv] Point-Cloud Saliency Maps. [tensorflow] [cls. oth.] [arXiv] Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers. [code] [oth.] [arxiv] Context Prediction for Unsupervised Deep Le...
[arXiv] Fast 3D Line Segment Detection From Unorganized Point Cloud. [det.] [arXiv] Point-Cloud Saliency Maps. [tensorflow] [cls. oth.] [arXiv] Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers. [code] [oth.] [arxiv] Context Prediction for Unsupervised Deep Le...