Search Results for author: Nikhil Gosala

Found 7 papers, 2 papers with code

Multi-camera Bird's Eye View Perception for Autonomous Driving

no code implementations16 Sep 2023 David Unger, Nikhil Gosala, Varun Ravi Kumar, Shubhankar Borse, Abhinav Valada, Senthil Yogamani

Surround vision systems that are pretty common in new vehicles use the IPM principle to generate a BEV image and to show it on display to the driver.

Autonomous Driving Sensor Fusion

A Smart Robotic System for Industrial Plant Supervision

no code implementations10 Aug 2023 D. Adriana Gómez-Rosal, Max Bergau, Georg K. J. Fischer, Andreas Wachaja, Johannes Gräter, Matthias Odenweller, Uwe Piechottka, Fabian Hoeflinger, Nikhil Gosala, Niklas Wetzel, Daniel Büscher, Abhinav Valada, Wolfram Burgard

In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous operating conditions.

Navigate

INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields

no code implementations31 Mar 2023 Julia Hindel, Nikhil Gosala, Kevin Bregler, Abhinav Valada

Perception datasets for agriculture are limited both in quantity and diversity which hinders effective training of supervised learning approaches.

Instance Segmentation object-detection +4

SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images

no code implementations CVPR 2023 Nikhil Gosala, Kürsat Petek, Paulo L. J. Drews-Jr, Wolfram Burgard, Abhinav Valada

Implicit supervision trains the model by enforcing spatial consistency of the scene over time based on FV semantic sequences, while explicit supervision exploits BEV pseudolabels generated from FV semantic annotations and self-supervised depth estimates.

Decision Making

Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation

no code implementations30 Sep 2021 Borna Bešić, Nikhil Gosala, Daniele Cattaneo, Abhinav Valada

Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill this domain gap and retain the performance of models on new sensor setups without the need for additional data labeling.

Autonomous Driving Navigate +3

Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images

1 code implementation6 Aug 2021 Nikhil Gosala, Abhinav Valada

Bird's-Eye-View (BEV) maps have emerged as one of the most powerful representations for scene understanding due to their ability to provide rich spatial context while being easy to interpret and process.

Depth Estimation Panoptic Segmentation +3

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