IEEE

Post-Training Quantization Based on Knowledge Distillation for Point Cloud 3D Object Detection

Abstract: Deploying LiDAR-based detectors on edge devices presents significant challenges due to limited computing power and memory. Post-training quantization (PTQ) requires only a small dataset for ...
IEEE

SP-Pillars: An Efficient LiDAR 3D Objects Detection Framework With Multi-Scale Feature Perception and Optimization

Abstract: In autonomous driving, achieving rapid detection of target categories and locations is a key technology. However, the data volume of radar point clouds is enormous, and processing efficiency ...
IEEE

FreeMask3D: Zero-Shot Point Cloud Instance Segmentation Without 3D Training

Abstract: Point cloud instance segmentation is crucial for 3D scene understanding in robotics. However, existing methods heavily rely on learning-based approaches that require large amounts of ...
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Photoshop tutorial: Vanishing point!

Photoshop cc tutorial showing how to use the Vanishing Point filter to place logos and other images in perspective onto photographs. 'Damage is already done': Premiums poised to spike Medical ...
IEEE

A 28nm 1.644TFLOPS/W Floating-Point Computation SRAM Macro with Variable Precision for Deep Neural Network Inference and Training

Abstract: This paper presents a digital compute-in-memory (CIM) macro for accelerating deep neural networks. The macro provides high-precision computation required for training deep neural networks ...