mlsanityData to 7 Oct 2026

MonoLite3D: Lightweight 3D Object Properties Estimation

arXiv 2503.02201 ↗First posted 4 Mar 2025cs.CVcited by 0

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Abstract (arXiv)

Reliable perception of the environment plays a crucial role in enabling efficient self-driving vehicles. Therefore, the perception system necessitates the acquisition of comprehensive 3D data regarding the surrounding objects within a specific time constrain, including their dimensions, spatial location and orientation. Deep learning has gained significant popularity in perception systems, enabling the conversion of image features captured by a camera into meaningful semantic information. This research paper introduces MonoLite3D network, an embedded-device friendly lightweight deep learning methodology designed for hardware environments with limited resources. MonoLite3D network is a cutting-edge technique that focuses on estimating multiple properties of 3D objects, encompassing their dimensions and spatial orientation, solely from monocular images. This approach is specifically designed to meet the requirements of resource-constrained environments, making it highly suitable for deployment on devices with limited computational capabilities. The experimental results validate the accuracy and efficiency of the proposed approach on the orientation benchmark of the KITTI dataset. It achieves an impressive score of 82.27% on the moderate class and 69.81% on the hard class, while still meeting the real-time requirements.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 56 references that are in the map. First posted on arXiv 4 Mar 2025.

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References · 35

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  3. 2015Fast R-CNNcited by 2,930
  4. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  5. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  6. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  7. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  8. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
  9. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 455
  10. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  11. 2019GhostNet: More Features from Cheap Operationscited by 410
  12. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 359
23 more
  1. 20163D Bounding Box Estimation Using Deep Learning and Geometrycited by 277
  2. 2019M3D-RPN: Monocular 3D Region Proposal Network for Object Detectioncited by 251
  3. 2019GA-Net: Guided Aggregation Net for End-to-end Stereo Matchingcited by 242
  4. 2020SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimationcited by 202
  5. 2017Learning a Multi-View Stereo Machinecited by 194
  6. 2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 190
  7. 2019Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Drivingcited by 188
  8. 2021Objects are Different: Flexible Monocular 3D Object Detectioncited by 184
  9. 2019Learning Depth-Guided Convolutions for Monocular 3D Object Detectioncited by 169
  10. 2020RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Drivingcited by 150
  11. 2019Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Drivingcited by 137
  12. 2022MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformercited by 108
  13. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  14. 2021Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detectioncited by 84
  15. 2021Ground-aware Monocular 3D Object Detection for Autonomous Drivingcited by 65
  16. 2020AFDet: Anchor Free One Stage 3D Object Detectioncited by 63
  17. 2018Anytime Stereo Image Depth Estimation on Mobile Devicescited by 54
  18. 2020Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimationcited by 43
  19. 2018RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinementcited by 31
  20. 2019PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Pointscited by 16
  21. 20203D IoU-Net: IoU Guided 3D Object Detector for Point Cloudscited by 15
  22. 2021MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentationcited by 15
  23. 2022Causal Scene BERT: Improving object detection by searching for challenging groups of datacited by 3

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