15 citations · 26 across the 10 of their papers we have counts for
13 papers
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model
Yucheng Mao, Boyang Wang, Nilesh Kulkarni +1
The computer vision community has developed numerous techniques for digitally restoring true scene information from single-view degraded photographs, an important yet extremely ill…
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface
Linyi Jin, Nilesh Kulkarni, David Fouhey
This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images. Designed to work with as few as one view, 3DFIRES reconstructs the complete geome…
FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation
Chris Rockwell, Nilesh Kulkarni, Linyi Jin +3
Estimating relative camera poses between images has been a central problem in computer vision. Methods that find correspondences and solve for the fundamental matrix offer high pre…
NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis
Nilesh Kulkarni, Davis Rempe, Kyle Genova +4
We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific…
Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data
Nilesh Kulkarni, Linyi Jin, Justin Johnson +1
We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB ima…
What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction
Nilesh Kulkarni, Justin Johnson, David F. Fouhey
We present an approach for full 3D scene reconstruction from a single unseen image. We train on dataset of realistic non-watertight scans of scenes. Our approach predicts a distanc…
Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry?
Alexander Raistrick, Nilesh Kulkarni, David F. Fouhey
What does bumping into things in a scene tell you about scene geometry? In this paper, we investigate the idea of learning from collisions. At the heart of our approach is the idea…
Implicit Mesh Reconstruction from Unannotated Image Collections
Shubham Tulsiani, Nilesh Kulkarni, Abhinav Gupta
We present an approach to infer the 3D shape, texture, and camera pose for an object from a single RGB image, using only category-level image collections with foreground masks as s…
Articulation-aware Canonical Surface Mapping
Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey +1
We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape, and 2) infe…
Canonical Surface Mapping via Geometric Cycle Consistency
Nilesh Kulkarni, Abhinav Gupta, Shubham Tulsiani
We explore the task of Canonical Surface Mapping (CSM). Specifically, given an image, we learn to map pixels on the object to their corresponding locations on an abstract 3D model…
3D-RelNet: Joint Object and Relational Network for 3D Prediction
Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani +1
We propose an approach to predict the 3D shape and pose for the objects present in a scene. Existing learning based methods that pursue this goal make independent predictions per o…
Syllable-level Neural Language Model for Agglutinative Language
Seunghak Yu, Nilesh Kulkarni, Haejun Lee +1
Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes. We propose a metho…
An Embedded Deep Learning based Word Prediction
Seunghak Yu, Nilesh Kulkarni, Haejun Lee +1
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation. However, deploying…