Bharath Hariharan

About

Bharath Hariharan is an associate professor in Computer Science at Cornell University specializing in computer vision and machine learning. His research focuses on challenging problems that do not fit the traditional "Big Data" paradigm, emphasizing the integration of advances in machine learning with insights from computer vision, geometry, and domain-specific knowledge. His work addresses fundamental challenges such as recognition on satellite images, which is critical for environmental and earth sciences but complicated by the lack of large labeled datasets. To tackle these challenges, his group has developed one of the most accurate foundation vision-language models for satellite images and novel self-supervised representations for this domain. In addition to satellite image recognition, Hariharan's research explores 4D reconstruction and recognition, aiming to understand dynamic scenes and long-term changes in the environment. His group investigates novel tracking formulations that follow individual pixels through long-term occlusions and objects through state changes, as well as new benchmarks and architectures for multimodal video understanding. His research has been recognized with prestigious awards including an NSF CAREER award and a PAMI Young Researcher Award, reflecting his significant contributions to the fields of computer vision and machine learning.

Research topics

• Artificial Intelligence
• Computer Science
• Remote sensing
• Computer vision
• Geography

Selected publications

• End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 202 citations

  Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D depth estimation with those for…

  DOI

• MegaScenes: Scene-Level View Synthesis at Scale

  Lecture notes in computer science · 2024-11-02 · 7 citations

  book-chapter
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• An Overview of the Significance of Cloud Computing in the Realm of the Metaverse

  Auerbach Publications eBooks · 2025-01-27 · 2 citations

  book-chapter1st authorCorresponding

  The metaverse, Blockchain technologies, and cloud computing converge to give rise to seamless connectivity and transformative digital opportunities. The term metaverse is derived from the words meta, meaning “beyond,” and universe, and can be understood as “outside the universe.” The metaverse pertains to the virtual shared space where users can interact with computer-generated environments, objects, and other users. Metaverse technology is powered by augmented reality (AR), virtual reality (VR)…

  PublisherDOI

• Accurate Differential Operators for Hybrid Neural Fields

  2025-06-10 · 1 citations

  articleSenior author

  Neural fields have become widely used in various fields, from shape representation to neural rendering, and for solving partial differential equations (PDEs). With the advent of hybrid neural field representations like Instant NGP that leverage small MLPs and explicit representations, these models train quickly and can fit large scenes. Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts. This is because they do not yield a…

  PublisherDOI

• Color Bind: Exploring Color Perception in Text-to-Image Models

  2026-03-06

  article

  Text-to-image generation has recently seen remarkable success, granting users with the ability to create high-quality images through the use of text. However, contemporary methods face challenges in capturing the precise semantics conveyed by complex multi-object prompts. Consequently, many works have sought to mitigate such semantic mis-alignments, typically via inference-time schemes that modify the attention layers of the denoising networks. However, prior work has mostly utilized coarse metr…

  PublisherDOI

Frequent coauthors

• Kilian Q. Weinberger

  37 shared

• Mark Campbell

  32 shared

• Ross Girshick

  30 shared

• Yurong You

  29 shared

• R. Moharana

  Indian Institute of Technology Jodhpur

  25 shared

• Sonali Gupta

  Graphic Era University

  25 shared

• Abhay Jain

  Saveetha University

  25 shared

• Pankaj Jain

  Indian Institute of Technology Kanpur

  25 shared

Labs

• Bharath Hariharan LabPI

  Computer vision and machine learning, particularly on problems that defy the 'Big Data' label.

Awards & honors

• PAMI Young Researcher Award
• IEEE Computer Society Bharath Hariharan Research 2022
• NSF Faculty Early Career Development Award (CAREER)

Similar researchers at Cornell University

• Jon Kleinbergh-122
• Deborah Estrinh-120
• Wen Sunh-102