lovefengqiang的博客

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features.We pro- pose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the hor- izontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our nal object detection system achieves an average precision of 37.3%, which is a 56% relative improve- ment over existing methods. We then focus on the task of instance seg- mentation where we label pixels belonging to object instances found by our detector. For this task, we propose a decision forest approach that classies pixels in the detection window as foreground or background us- ing a family of unary and binary tests that query shape and geocentric pose features. Finally, we use the output from our object detectors in an existing superpixel classication framework for semantic scene segmenta- tion and achieve a 24% relative improvement over current state-of-the-art for the object categories that we study.We believe advances such as those represented in this paper will facilitate the use of perception in elds like robotics.

Abstract— This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L1 regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that Vote3Deep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40% while remaining highly competitive in terms of processing time.

Recent advances in 3D sensing technologies make it possible to easily record color and depth images which together can improve object recognition. Most current methods rely on very well-designed features for this new 3D modality. We introduce a model based on a combination of convolutional and recursive neural networks (CNN and RNN) for learning features and classifying RGB-D images.The CNN layer learns low-level translationally invariant features which are then given as inputs to multiple, fixed-tree RNNs in order to compose higher order features. RNNs can be seen as combining convolution and pooling into one efficient,hierarchical operation. Our main result is that even RNNs with random weights compose powerful features. Our model obtains state of the art performance on a standard RGB-D object dataset while being more accurate and faster during training and testing than comparable architectures such as two-layer CNNs.

DWARF is a debugging file format used by many compilers and debuggers to support source level debugging. It addresses the requirements of a number of procedural languages, such as C, C++, and Fortran, and is designed to be extensible to other languages. DWARF is architecture independent and applicable to any processor or operating system. It is widely used on Unix, Linux and other operating systems, as well as in stand-alone environments.

MIPS的汇编语言程序设计教程

FPGA设计中的编程技巧

使用C和Gnu开发工具 英文原版

数字电路 VHDL 数字电路设计 数字电路 VHDL 数字电路设计数字电路 VHDL 数字电路设计

Xilinx FPGA 最新培训讲义，分不同的部分，英文讲义

讲解ARM汇编指令、体系结构方面，对于通用处理器的相关概念也有介绍，最后是外围电路设计与编程

完整介绍了数字设计相关知识，并用VHDL实现完整介绍了数字设计相关知识，并用VHDL实现

用C语言实现的内存管理，主要是堆和栈的管理 以及内存回收等算法的实现

转载，MP4编解源代码,PC上运行，首先需要了解MP4编解码原理

VHDL编写的自动售货机，有退毕，找零功能

步进电机Linux驱动，对学习linux和步进电机原理很有帮助

FPGA实现CAN总线源码，对学习FPGA和CAN总线协议很有帮助