Search Results for author: Siddharth Ancha

Found 8 papers, 3 papers with code

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.

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).

Robust Instance Tracking via Uncertainty Flow

no code implementations9 Oct 2020 Jianing Qian, Junyu Nan, Siddharth Ancha, Brian Okorn, David Held

Current state-of-the-art trackers often fail due to distractorsand large object appearance changes.

Optical Flow Estimation

Uncertainty-aware Self-supervised 3D Data Association

1 code implementation18 Aug 2020 Jianren Wang, Siddharth Ancha, Yi-Ting Chen, David Held

Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association.

Metric Learning Object Detection

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

Combining Deep Learning and Verification for Precise Object Instance Detection

2 code implementations27 Dec 2019 Siddharth Ancha, Junyu Nan, David Held

For a reliable detection system, if a high confidence detection is made, we would want high certainty that the object has indeed been detected.

Autofocus Layer for Semantic Segmentation

3 code implementations22 May 2018 Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison Cottrell, Antonio Criminisi, Aditya Nori

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.

Brain Tumor Segmentation Tumor Segmentation

Measuring the reliability of MCMC inference with bidirectional Monte Carlo

no code implementations NeurIPS 2016 Roger B. Grosse, Siddharth Ancha, Daniel M. Roy

Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples.

Probabilistic Programming

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