mlsanityData to 7 Oct 2026

SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics

arXiv 2403.06501 ↗First posted 11 Mar 2024cs.CVcited by 3

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Abstract (arXiv)

In autonomous driving, 3D object detection provides more precise information for downstream tasks, including path planning and motion estimation, compared to 2D object detection. In this paper, we propose SeSame: a method aimed at enhancing semantic information in existing LiDAR-only based 3D object detection. This addresses the limitation of existing 3D detectors, which primarily focus on object presence and classification, thus lacking in capturing relationships between elemental units that constitute the data, akin to semantic segmentation. Experiments demonstrate the effectiveness of our method with performance improvements on the KITTI object detection benchmark. Our code is available at https://github.com/HAMA-DL-dev/SeSame

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 41 references that are in the map. First posted on arXiv 11 Mar 2024.

Builds on

32 of 41 references.

References · 32

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  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
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  12. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
20 more
  1. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  2. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  3. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  4. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  5. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  6. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 455
  7. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  8. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 388
  9. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
  10. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  11. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  12. 2018A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decaycited by 223
  13. 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
  14. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  15. 2022Vision-Centric BEV Perception: A Surveycited by 126
  16. 2019SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Drivingcited by 57
  17. 2019PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Modulecited by 56
  18. 2020Tensor Low-Rank Reconstruction for Semantic Segmentationcited by 29
  19. 2018PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloudcited by 19
  20. 2023Surround-View Vision-based 3D Detection for Autonomous Driving: A Surveycited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detectioncited by 1
  2. 2025StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloudcited by 0
  3. 2026Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detectioncited by 0