1.2k citations · 4.3k across the 84 of their papers we have counts for
27 papers · 1 filter
ORCa: Glossy Objects as Radiance Field Cameras
Kushagra Tiwary, Akshat Dave, Nikhil Behari +3
Reflections on glossy objects contain valuable and hidden information about the surrounding environment. By converting these objects into cameras, we can unlock exciting applicatio…
Detection and Mapping of Specular Surfaces Using Multibounce Lidar Returns
Connor Henley, Siddharth Somasundaram, Joseph Hollmann +1
We propose methods that use specular, multibounce lidar returns to detect and map specular surfaces that might be invisible to conventional lidar systems that rely on direct, singl…
Physically Disentangled Representations
Tzofi Klinghoffer, Kushagra Tiwary, Arkadiusz Balata +2
State-of-the-art methods in generative representation learning yield semantic disentanglement, but typically do not consider physical scene parameters, such as geometry, albedo, li…
Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging
Tzofi Klinghoffer, Siddharth Somasundaram, Kushagra Tiwary +1
Cameras were originally designed using physics-based heuristics to capture aesthetic images. In recent years, there has been a transformation in camera design from being purely phy…
Learning to Censor by Noisy Sampling
Ayush Chopra, Abhinav Java, Abhishek Singh +2
Point clouds are an increasingly ubiquitous input modality and the raw signal can be efficiently processed with recent progress in deep learning. This signal may, often inadvertent…
Towards Learning Neural Representations from Shadows
Kushagra Tiwary, Tzofi Klinghoffer, Ramesh Raskar
We present a method that learns neural shadow fields which are neural scene representations that are only learnt from the shadows present in the scene. While traditional shape-from…
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
Abhishek Singh, Ayush Chopra, Vivek Sharma +4
Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assu…
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al.…
Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning
Ilke Demir, Ramesh Raskar
More than half of the world's roads lack adequate street addressing systems. Lack of addresses is even more visible in daily lives of people in developing countries. We would like…
Flash Photography for Data-Driven Hidden Scene Recovery
Matthew Tancik, Guy Satat, Ramesh Raskar
Vehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these ta…
3D Traffic Simulation for Autonomous Vehicles in Unity and Python
Zhijing Jin, Tristan Swedish, Ramesh Raskar
Over the recent years, there has been an explosion of studies on autonomous vehicles. Many collected large amount of data from human drivers. However, compared to the tedious data…
Maximum-Entropy Fine-Grained Classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar +1
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators t…
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
Ilke Demir, Krzysztof Koperski, David Lindenbaum +6
We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite i…
Pairwise Confusion for Fine-Grained Visual Classification
Abhimanyu Dubey, Otkrist Gupta, Pei Guo +3
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has address…
Lensless Imaging with Compressive Ultrafast Sensing
Guy Satat, Matthew Tancik, Ramesh Raskar
Lensless imaging is an important and challenging problem. One notable solution to lensless imaging is a single pixel camera which benefits from ideas central to compressive samplin…
Deep Learning the City : Quantifying Urban Perception At A Global Scale
Abhimanyu Dubey, Nikhil Naik, Devi Parikh +2
Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city's physical appearance and the behavio…
Shape from Mixed Polarization
Vage Taamazyan, Achuta Kadambi, Ramesh Raskar
Shape from Polarization (SfP) estimates surface normals using photos captured at different polarizer rotations. Fundamentally, the SfP model assumes that light is reflected either…
Sweep Distortion Removal from THz Images via Blind Demodulation
Alireza Aghasi, Barmak Heshmat, Albert Redo-Sanchez +2
Heavy sweep distortion induced by alignments and inter-reflections of layers of a sample is a major burden in recovering 2D and 3D information in time resolved spectral imaging. Th…
Multi-velocity neural networks for gesture recognition in videos
Otkrist Gupta, Dan Raviv, Ramesh Raskar
We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification. While deep neural networks have reache…
Deep video gesture recognition using illumination invariants
Otkrist Gupta, Dan Raviv, Ramesh Raskar
In this paper we present architectures based on deep neural nets for gesture recognition in videos, which are invariant to local scaling. We amalgamate autoencoder and predictor ar…
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
G. Leifman, D. Rudoy, T. Swedish +2
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative con…
Coreset-Based Adaptive Tracking
Abhimanyu Dubey, Nikhil Naik, Dan Raviv +2
We propose a method for learning from streaming visual data using a compact, constant size representation of all the data that was seen until a given moment. Specifically, we const…
Frequency Domain TOF: Encoding Object Depth in Modulation Frequency
Achuta Kadambi, Vage Taamazyan, Suren Jayasuriya +1
Time of flight cameras may emerge as the 3-D sensor of choice. Today, time of flight sensors use phase-based sampling, where the phase delay between emitted and received, high-freq…
A Light Transport Model for Mitigating Multipath Interference in TOF Sensors
Nikhil Naik, Achuta Kadambi, Christoph Rhemann +3
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics. However, the depth images obt…