Search Results for author: Srinivasa G. Narasimhan

Found 25 papers, 5 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.

WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion

no code implementations27 Mar 2024 Khiem Vuong, N. Dinesh Reddy, Robert Tamburo, Srinivasa G. Narasimhan

Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion.

Addressing Source Scale Bias via Image Warping for Domain Adaptation

no code implementations19 Mar 2024 Shen Zheng, Anurag Ghosh, Srinivasa G. Narasimhan

Discovering that shifting the source scale distribution improves backbone features, we developed a instance-level warping guidance aimed at object region sampling to mitigate source scale bias in domain adaptation.

Domain Adaptation Object

Virtual Home Staging: Inverse Rendering and Editing an Indoor Panorama under Natural Illumination

1 code implementation21 Nov 2023 Guanzhou Ji, Azadeh O. Sawyer, Srinivasa G. Narasimhan

We propose a novel inverse rendering method that enables the transformation of existing indoor panoramas with new indoor furniture layouts under natural illumination.

Inverse Rendering Layout Design

Toward Planet-Wide Traffic Camera Calibration

no code implementations6 Nov 2023 Khiem Vuong, Robert Tamburo, Srinivasa G. Narasimhan

Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges.

3D Scene Reconstruction Camera Calibration

TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain

1 code implementation1 Nov 2023 Shen Zheng, Changjie Lu, Srinivasa G. Narasimhan

We first introduce a Triangular Probability Similarity (TPS) constraint to guide the generated images toward clear and rainy images in the discriminator manifold, thereby minimizing artifacts and distortions during rain generation.

Contrastive Learning Image-to-Image Translation +6

Learned Two-Plane Perspective Prior based Image Resampling for Efficient Object Detection

no code implementations CVPR 2023 Anurag Ghosh, N. Dinesh Reddy, Christoph Mertz, Srinivasa G. Narasimhan

For autonomous navigation, using the same detector and scale, our approach improves detection rate by +4. 1 $AP_{S}$ or +39% and in real-time performance by +5. 3 $sAP_{S}$ or +63% for small objects over state-of-the-art (SOTA).

Autonomous Navigation object-detection +1

Megahertz Light Steering Without Moving Parts

no code implementations CVPR 2023 Adithya Pediredla, Srinivasa G. Narasimhan, Maysamreza Chamanzar, Ioannis Gkioulekas

We introduce a light steering technology that operates at megahertz frequencies, has no moving parts, and costs less than a hundred dollars.

Analyzing Physical Impacts Using Transient Surface Wave Imaging

no code implementations CVPR 2023 Tianyuan Zhang, Mark Sheinin, Dorian Chan, Mark Rau, Matthew O’Toole, Srinivasa G. Narasimhan

The subtle vibrations on an object's surface contain information about the object's physical properties and its interaction with the environment.

Object

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.

Amodal Instance Segmentation Amodal Tracking +4

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.

Navigate

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 Point Tracking

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.

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

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 +1

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