Search Results for author:
Found 20 papers, 6 papers with code
Learning from Maps: Visual Common Sense for Autonomous Driving
Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment.
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
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.
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
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
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
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.
ShapeNet: An Information-Rich 3D Model Repository
We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects.
Large Scale Artificial Neural Network Training Using Multi-GPUs
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.
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
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)
BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing
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
Robot In a Room: Toward Perfect Object Recognition in Closed Environments
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.
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
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.
SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite
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.
Semantic Alignment of LiDAR Data at City Scale
This paper describes an automatic algorithm for global alignment of LiDAR data collected with Google Street View cars in urban environments.
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
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.
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow.
Learning Deep Features for Scene Recognition using Places Database
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.
3D ShapeNets: A Deep Representation for Volumetric Shapes
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)
Local Readjustment for High-Resolution 3D Reconstruction
To this end, we propose a segment-based approach to readjust the camera poses locally and improve the reconstruction for fine geometry details.
FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps
We analyze the self-similarity of the guide image to generate a set of allowable local transformations and apply them to the input image.
A Linear Approach to Matching Cuboids in RGBD Images
We propose a novel linear method to match cuboids in indoor scenes using RGBD images from Kinect.
Localizing 3D cuboids in single-view images
In this paper we seek to detect rectangular cuboids and localize their corners in uncalibrated single-view images depicting everyday scenes.
