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Evaluation of Multimodal Semantic Segmentation using RGB-D Data

arXiv 2103.16758 ↗First posted 31 Mar 2021cs.CV cs.AIcited by 0

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Abstract (arXiv)

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environments. To achieve this, we are exploring and evaluating a range of related technology and solutions, including AI-driven multimodal scene perception, fusion, processing, and understanding. This work reports our efforts on the evaluation of a state-of-the-art approach for semantic segmentation with multiple RGB and depth sensing data. We employ four large datasets composed of diverse urban and terrain scenes and design various experimental methods and metrics. In addition, we also develop new strategies of multi-datasets learning to improve the detection and recognition of unseen objects. Extensive experiments, implementations, and results are reported in the paper.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 31 Mar 2021.

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21 of 30 references.

References · 21

  1. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  4. 2016Pyramid Scene Parsing Networkcited by 2,472
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  6. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  7. 2017Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenescited by 122
  8. 2020RELLIS-3D Dataset: Data, Benchmarks and Analysiscited by 118
  9. 2016Lost and Found: Detecting Small Road Hazards for Self-Driving Vehiclescited by 117
  10. 2019ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentationcited by 105
  11. 2019In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Imagescited by 99
  12. 2018Depth-aware CNN for RGB-D Segmentationcited by 73
9 more
  1. 2013Indoor Semantic Segmentation using depth informationcited by 56
  2. 2020PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classificationcited by 37
  3. 2020Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Imagescited by 36
  4. 2019FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Datacited by 33
  5. 2016Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modelingcited by 28
  6. 2020LiDAR guided Small obstacle Segmentationcited by 17
  7. 2021CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networkscited by 14
  8. 2019RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentationcited by 12
  9. 2018MergeNet: A Deep Net Architecture for Small Obstacle Discoverycited by 11

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