68 citations · 116 across the 7 of their papers we have counts for
14 papers
-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception
Dhaivat Bhatt, Kaustubh Mani, Dishank Bansal +3
While modern deep neural networks are performant perception modules, performance (accuracy) alone is insufficient, particularly for safety-critical robotic applications such as sel…
AutoLay: Benchmarking amodal layout estimation for autonomous driving
Kaustubh Mani, N. Sai Shankar, Krishna Murthy Jatavallabhula +1
Given an image or a video captured from a monocular camera, amodal layout estimation is the task of predicting semantics and occupancy in bird's eye view. The term amodal implies w…
gradSim: Differentiable simulation for system identification and visuomotor control
Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo +11
We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences. Such a system identification problem is…
DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects
Rahul Sajnani, AadilMehdi Sanchawala, Krishna Murthy Jatavallabhula +2
We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images. Canonical shape reconstruction, estimating 3D object shape in…
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang +3
The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have larg…
MonoLayout: Amodal scene layout from a single image
Kaustubh Mani, Swapnil Daga, Shubhika Garg +3
In this paper, we address the novel, highly challenging problem of estimating the layout of a complex urban driving scenario. Given a single color image captured from a driving pla…