2.4k citations · 4.4k across the 15 of their papers we have counts for
11 papers · 1 filter
Learning from Maps: Visual Common Sense for Autonomous Driving
Ari Seff, Jianxiong Xiao
Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment. While this approach works well when these maps are completely up-to-date, safe…
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
Andy Zeng, Kuan-Ting Yu, Shuran Song +4
Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully autonomous warehouse pick-and-pla…
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Andy Zeng, Shuran Song, Matthias Nießner +3
Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit…
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
Yinda Zhang, Mingru Bai, Pushmeet Kohli +2
While deep neural networks have led to human-level performance on computer vision tasks, they have yet to demonstrate similar gains for holistic scene understanding. In particular,…
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
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. We introduce Deep Slidin…
Robot In a Room: Toward Perfect Object Recognition in Closed Environments
Shuran Song, Linguang Zhang, Jianxiong Xiao
While general object recognition is still far from being solved, this paper proposes a way for a robot to recognize every object at an almost human-level accuracy. Our key observat…
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Fisher Yu, Ari Seff, Yinda Zhang +3
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. Large labeled traini…
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
Pingmei Xu, Krista A Ehinger, Yinda Zhang +3
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow. Therefore, existing saliency prediction…
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Chenyi Chen, Ari Seff, Alain Kornhauser +1
Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior…
3D ShapeNets: A Deep Representation for Volumetric Shapes
Zhirong Wu, Shuran Song, Aditya Khosla +4
3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability…
Tracking Revisited using RGBD Camera: Baseline and Benchmark
Shuran Song, Jianxiong Xiao
Although there has been significant progress in the past decade,tracking is still a very challenging computer vision task, due to problems such as occlusion and model drift.Recentl…