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Found 16 papers, 4 papers with code
Progressive Multi-Modal Fusion for Robust 3D Object Detection
Multi-sensor fusion is crucial for accurate 3D object detection in autonomous driving, with cameras and LiDAR being the most commonly used sensors.
Amodal Optical Flow
Optical flow estimation is very challenging in situations with transparent or occluded objects.
Panoptic Out-of-Distribution Segmentation
Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task.
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving
To address these limitations, we introduce AmodalSynthDrive, a synthetic multi-task multi-modal amodal perception dataset.
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities
Safety and efficiency are paramount in healthcare facilities where the lives of patients are at stake.
Perceiving the Invisible: Proposal-Free Amodal Panoptic Segmentation
Amodal panoptic segmentation aims to connect the perception of the world to its cognitive understanding.
Amodal Panoptic Segmentation
To enable robots to reason with this capability, we formulate and propose a novel task that we name amodal panoptic segmentation.
Ranked #1 on
Amodal Panoptic Segmentation
on BDD100K val
7th AI Driving Olympics: 1st Place Report for Panoptic Tracking
In this technical report, we describe our EfficientLPT architecture that won the panoptic tracking challenge in the 7th AI Driving Olympics at NeurIPS 2021.
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments.
Ranked #1 on
Panoptic Segmentation
on Panoptic nuScenes test
EfficientLPS: Efficient LiDAR Panoptic Segmentation
Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors.
Robust Vision Challenge 2020 -- 1st Place Report for Panoptic Segmentation
In this technical report, we present key details of our winning panoptic segmentation architecture EffPS_b1bs4_RVC.
MOPT: Multi-Object Panoptic Tracking
In this paper, we introduce a novel perception task denoted as multi-object panoptic tracking (MOPT), which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking.
EfficientPS: Efficient Panoptic Segmentation
Understanding the scene in which an autonomous robot operates is critical for its competent functioning.
Ranked #1 on
Panoptic Segmentation
on Indian Driving Dataset
Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue
This problem is extremely challenging as pre-existing maps cannot be leveraged for navigation due to structural changes that may have occurred.
Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network
Indoor localization is one of the crucial enablers for deployment of service robots.
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner.
Ranked #1 on
Scene Recognition
on ScanNet
