2.4k citations · 8.4k across the 129 of their papers we have counts for
17 papers · 1 filter
Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
Shuran Song, Andy Zeng, Angel X. Chang +3
We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic vi…
CAR-Net: Clairvoyant Attentive Recurrent Network
Amir Sadeghian, Ferdinand Legros, Maxime Voisin +3
We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of…
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Riccardo Volpi, Pietro Morerio, Silvio Savarese +1
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabele…
Recurrent Autoregressive Networks for Online Multi-Object Tracking
Kuan Fang, Yu Xiang, Xiaocheng Li +1
The main challenge of online multi-object tracking is to reliably associate object trajectories with detections in each video frame based on their tracking history. In this work, w…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
Generic 3D Representation via Pose Estimation and Matching
Amir R. Zamir, Tilman Wekel, Pulkit Argrawal +3
Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been li…
SEGCloud: Semantic Segmentation of 3D Point Clouds
Lyne P. Tchapmi, Christopher B. Choy, Iro Armeni +2
3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent work…
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
Danfei Xu, Suraj Nair, Yuke Zhu +4
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program in…
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation
Noriaki Hirose, Amir Sadeghian, Patrick Goebel +1
It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid inju…
Lattice Long Short-Term Memory for Human Action Recognition
Lin Sun, Kui Jia, Kevin Chen +3
Human actions captured in video sequences are three-dimensional signals characterizing visual appearance and motion dynamics. To learn action patterns, existing methods adopt Convo…
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
Andrey Kurenkov, Jingwei Ji, Animesh Garg +4
3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem thro…
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener, Silvio Savarese
Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset o…
ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems
James Harrison, Animesh Garg, Boris Ivanovic +4
Model-free policy learning has enabled robust performance of complex tasks with relatively simple algorithms. However, this simplicity comes at the cost of requiring an Oracle and…
Weakly supervised 3D Reconstruction with Adversarial Constraint
JunYoung Gwak, Christopher B. Choy, Animesh Garg +2
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of…
Deep View Morphing
Dinghuang Ji, Junghyun Kwon, Max McFarland +1
Recently, convolutional neural networks (CNN) have been successfully applied to view synthesis problems. However, such CNN-based methods can suffer from lack of texture details, sh…
Joint 2D-3D-Semantic Data for Indoor Scene Understanding
Iro Armeni, Sasha Sax, Amir R. Zamir +1
We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric…
Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies
Amir Sadeghian, Alexandre Alahi, Silvio Savarese
The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues in a coherent end-to-end fashion over a long period of time. However, we present a…