no code implementations • 25 Nov 2016 • Ari Seff, Jianxiong Xiao
Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment.
2 code implementations • 29 Sep 2016 • Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr., Alberto Rodriguez, Jianxiong Xiao
The approach was part of the MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and picking tasks, respectively at APC 2016.
2 code implementations • CVPR 2017 • Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser
To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions.
Ranked #2 on 3D Reconstruction on Scan2CAD
no code implementations • ICCV 2017 • Yinda Zhang, Mingru Bai, Pushmeet Kohli, Shahram Izadi, Jianxiong Xiao
In particular, 3D context has been shown to be an extremely important cue for scene understanding - yet very little research has been done on integrating context information with deep models.
14 code implementations • 9 Dec 2015 • Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, Fisher Yu
We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects.
no code implementations • 13 Nov 2015 • Linnan Wang, Wei Wu, Jianxiong Xiao, Yang Yi
This paper describes a method for accelerating large scale Artificial Neural Networks (ANN) training using multi-GPUs by reducing the forward and backward passes to matrix multiplication.
no code implementations • CVPR 2016 • Shuran Song, Jianxiong Xiao
We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent.
Ranked #6 on 3D Object Detection on SUN-RGBD val (Inference Speed (s) metric)
1 code implementation • 16 Oct 2015 • Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang
Basic Linear Algebra Subprograms (BLAS) are a set of low level linear algebra kernels widely adopted by applications involved with the deep learning and scientific computing.
Distributed, Parallel, and Cluster Computing
no code implementations • 9 Jul 2015 • Shuran Song, Linguang Zhang, Jianxiong Xiao
By constraining a robot to stay in a limited territory, we can ensure that the robot has seen most objects before and the speed of introducing a new object is slow.
4 code implementations • 10 Jun 2015 • Fisher Yu, Ari Seff, yinda zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao
While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry.
no code implementations • CVPR 2015 • Shuran Song, Samuel P. Lichtenberg, Jianxiong Xiao
Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding.
no code implementations • CVPR 2015 • Fisher Yu, Jianxiong Xiao, Thomas Funkhouser
This paper describes an automatic algorithm for global alignment of LiDAR data collected with Google Street View cars in urban environments.
no code implementations • ICCV 2015 • Chenyi Chen, Ari Seff, Alain Kornhauser, Jianxiong Xiao
To demonstrate this, we train a deep Convolutional Neural Network using recording from 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments.
1 code implementation • 25 Apr 2015 • Pingmei Xu, Krista A. Ehinger, yinda zhang, Adam Finkelstein, Sanjeev R. Kulkarni, Jianxiong Xiao
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow.
no code implementations • NeurIPS 2014 • Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva
Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success.
no code implementations • CVPR 2015 • Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao
Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.
Ranked #31 on 3D Point Cloud Classification on ModelNet40 (Mean Accuracy metric)
no code implementations • CVPR 2014 • Siyu Zhu, Tian Fang, Jianxiong Xiao, Long Quan
To this end, we propose a segment-based approach to readjust the camera poses locally and improve the reconstruction for fine geometry details.
no code implementations • CVPR 2013 • Yinda Zhang, Jianxiong Xiao, James Hays, Ping Tan
We analyze the self-similarity of the guide image to generate a set of allowable local transformations and apply them to the input image.
no code implementations • CVPR 2013 • Hao Jiang, Jianxiong Xiao
We propose a novel linear method to match cuboids in indoor scenes using RGBD images from Kinect.
no code implementations • NeurIPS 2012 • Jianxiong Xiao, Bryan Russell, Antonio Torralba
In this paper we seek to detect rectangular cuboids and localize their corners in uncalibrated single-view images depicting everyday scenes.