mlsanityData to 13 Oct 2026

Real-time 3D Object Detection using Feature Map Flow

arXiv 2106.14101 ↗First posted 26 Jun 2021cs.CV cs.AIcited by 5

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Abstract (arXiv)

In this paper, we present a real-time 3D detection approach considering time-spatial feature map aggregation from different time steps of deep neural model inference (named feature map flow, FMF). Proposed approach improves the quality of 3D detection center-based baseline and provides real-time performance on the nuScenes and Waymo benchmark. Code is available at https://github.com/YoushaaMurhij/FMFNet

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 26 Jun 2021.

Builds on

12 of 16 references.

References · 12

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,625
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,231
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,498
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,709
  5. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  6. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  7. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 812
  8. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  9. 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
  10. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  11. 2019Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspotscited by 84
  12. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 1.

5 papers.

  1. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  2. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
  3. 2022A Survey of Robust 3D Object Detection Methods in Point Cloudscited by 10
  4. 2022Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Cloudscited by 6
  5. 2023Pillar R-CNN for Point Cloud 3D Object Detectioncited by 2