Search Results for author: Srinivasa G. Narasimhan

Found 17 papers, 3 papers with code

Diffraction Line Imaging

no code implementations ECCV 2020 Mark Sheinin, Dinesh N. Reddy, Matthew O’Toole, Srinivasa G. Narasimhan

Thus, our system is able to achieve high-speed and high-accuracy 2D positioning of light sources and 3D scanning of scenes.

Learning Continuous Implicit Representation for Near-Periodic Patterns

1 code implementation25 Aug 2022 Bowei Chen, Tiancheng Zhi, Martial Hebert, Srinivasa G. Narasimhan

To address these challenges, we learn a neural implicit representation using a coordinate-based MLP with single image optimization.

Dual-Shutter Optical Vibration Sensing

no code implementations CVPR 2022 Mark Sheinin, Dorian Chan, Matthew O'Toole, Srinivasa G. Narasimhan

Visual vibrometry is a highly useful tool for remote capture of audio, as well as the physical properties of materials, human heart rate, and more.

WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery

1 code implementation CVPR 2022 N. Dinesh Reddy, Robert Tamburo, Srinivasa G. Narasimhan

Labeled real data of occlusions is scarce (even in large datasets) and synthetic data leaves a domain gap, making it hard to explicitly model and learn occlusions.

2D object detection Amodal Instance Segmentation +5

Holocurtains: Programming Light Curtains via Binary Holography

no code implementations CVPR 2022 Dorian Chan, Srinivasa G. Narasimhan, Matthew O'Toole

Light curtain systems are designed for detecting the presence of objects within a user-defined 3D region of space, which has many applications across vision and robotics.

Active Safety Envelopes using Light Curtains with Probabilistic Guarantees

no code implementations8 Jul 2021 Siddharth Ancha, Gaurav Pathak, Srinivasa G. Narasimhan, David Held

We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles.


Exploiting & Refining Depth Distributions With Triangulation Light Curtains

no code implementations CVPR 2021 Yaadhav Raaj, Siddharth Ancha, Robert Tamburo, David Held, Srinivasa G. Narasimhan

Active sensing through the use of Adaptive Depth Sensors is a nascent field, with potential in areas such as Advanced driver-assistance systems (ADAS).

Active Perception using Light Curtains for Autonomous Driving

no code implementations ECCV 2020 Siddharth Ancha, Yaadhav Raaj, Peiyun Hu, Srinivasa G. Narasimhan, David Held

Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data.

3D Object Recognition Autonomous Driving

TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video

no code implementations ECCV 2020 Tiancheng Zhi, Christoph Lassner, Tony Tung, Carsten Stoll, Srinivasa G. Narasimhan, Minh Vo

We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video.

Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild

no code implementations24 Jul 2020 Minh Vo, Yaser Sheikh, Srinivasa G. Narasimhan

The triangulation constraint, however, is invalid for moving points captured in multiple unsynchronized videos and bundle adjustment is not designed to estimate the temporal alignment between cameras.

3D Human Reconstruction

Programmable Triangulation Light Curtains

no code implementations ECCV 2018 Jian Wang, Joseph Bartels, William Whittaker, Aswin C. Sankaranarayanan, Srinivasa G. Narasimhan

A vehicle on a road or a robot in the field does not need a full-featured 3D depth sensor to detect potential collisions or monitor its blind spot.

Deep Material-Aware Cross-Spectral Stereo Matching

no code implementations CVPR 2018 Tiancheng Zhi, Bernardo R. Pires, Martial Hebert, Srinivasa G. Narasimhan

Often, multiple cameras are used for cross-spectral imaging, thus requiring image alignment, or disparity estimation in a stereo setting.

Disparity Estimation Stereo Matching +1

CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles

1 code implementation CVPR 2018 N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan

In this work, we develop a framework to fuse both the single-view feature tracks and multi-view detected part locations to significantly improve the detection, localization and reconstruction of moving vehicles, even in the presence of strong occlusions.

3D Reconstruction

Matting and Depth Recovery of Thin Structures Using a Focal Stack

no code implementations CVPR 2017 Chao Liu, Srinivasa G. Narasimhan, Artur W. Dubrawski

For macro-scale, we evaluate our method on scenes with complex 3D thin structures such as tree branches and grass.

Image Matting

The Geometry of First-Returning Photons for Non-Line-Of-Sight Imaging

no code implementations CVPR 2017 Chia-Yin Tsai, Kiriakos N. Kutulakos, Srinivasa G. Narasimhan, Aswin C. Sankaranarayanan

In this paper, we propose a new approach for NLOS imaging by studying the properties of first-returning photons from three-bounce light paths.

Spatiotemporal Bundle Adjustment for Dynamic 3D Reconstruction

no code implementations CVPR 2016 Minh Vo, Srinivasa G. Narasimhan, Yaser Sheikh

In this paper, we present a spatiotemporal bundle adjustment approach that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating 3D static points, as well as subframe temporal alignment between cameras and estimating 3D trajectories of dynamic points.

3D Reconstruction Dynamic Reconstruction

Simultaneous Estimation of Near IR BRDF and Fine-Scale Surface Geometry

no code implementations CVPR 2016 Gyeongmin Choe, Srinivasa G. Narasimhan, In So Kweon

Near-Infrared (NIR) images of most materials exhibit less texture or albedo variations making them beneficial for vision tasks such as intrinsic image decomposition and structured light depth estimation.

Depth Estimation Intrinsic Image Decomposition

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