15 citations · 15 across the 3 of their papers we have counts for
7 papers
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…