mlsanityData to 7 Oct 2026

AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation

arXiv 2012.04934 ↗First posted 9 Dec 2020cs.CV cs.LG cs.ROcited by 87

Not on the map. Why?

Abstract (arXiv)

In this paper, we present an Assertion-based Multi-View Fusion network (AMVNet) for LiDAR semantic segmentation which aggregates the semantic features of individual projection-based networks using late fusion. Given class scores from different projection-based networks, we perform assertion-guided point sampling on score disagreements and pass a set of point-level features for each sampled point to a simple point head which refines the predictions. This modular-and-hierarchical late fusion approach provides the flexibility of having two independent networks with a minor overhead from a light-weight network. Such approaches are desirable for robotic systems, e.g. autonomous vehicles, for which the computational and memory resources are often limited. Extensive experiments show that AMVNet achieves state-of-the-art results in both the SemanticKITTI and nuScenes benchmark datasets and that our approach outperforms the baseline method of combining the class scores of the projection-based networks.

Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 17; 2023: 25; 2024: 21; 2025: 15; 2026: 2.

87 papers.

Sort:

By area:

  1. 2023Tri-Perspective View for Vision-Based 3D Semantic Occupancy Predictioncited by 404
  2. 2023SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Drivingcited by 310
  3. 2023OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Predictioncited by 287
  4. 2023OccWorld: Learning a 3D Occupancy World Model for Autonomous Drivingcited by 230
  5. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  6. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
  7. 2021Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Trackingcited by 165
  8. 2021(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Networkcited by 164
  9. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 157
  10. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 128
  11. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 126
  12. 2023Spherical Transformer for LiDAR-based 3D Recognitioncited by 124
38 more
  1. 2023Segment Any Point Cloud Sequences by Distilling Vision Foundation Modelscited by 123
  2. 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
  3. 2023PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentationcited by 114
  4. 2022LaserMix for Semi-Supervised LiDAR Semantic Segmentationcited by 92
  5. 2024GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Predictioncited by 83
  6. 2022LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perceptioncited by 74
  7. 2022Scribble-Supervised LiDAR Semantic Segmentationcited by 69
  8. 2023MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Drivingcited by 68
  9. 2021EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentationcited by 59
  10. 2023UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebasecited by 57
  11. 2023Using a Waffle Iron for Automotive Point Cloud Semantic Segmentationcited by 53
  12. 2024Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Drivingcited by 53
  13. 2023PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Predictioncited by 51
  14. 2023FRNet: Frustum-Range Networks for Scalable LiDAR Segmentationcited by 50
  15. 2021FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decodingcited by 46
  16. 2022LESS: Label-Efficient Semantic Segmentation for LiDAR Point Cloudscited by 41
  17. 2024Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Drivingcited by 39
  18. 2022GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentationcited by 36
  19. 2021LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentationcited by 34
  20. 2023Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentationcited by 33
  21. 2024OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Predictioncited by 32
  22. 20244D Contrastive Superflows are Dense 3D Representation Learnerscited by 32
  23. 2022Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Cloudscited by 31
  24. 2025Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Drivingcited by 29
  25. 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 25
  26. 2024Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunitiescited by 24
  27. 2024Multi-Space Alignments Towards Universal LiDAR Segmentationcited by 24
  28. 2022Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregationcited by 23
  29. 2024An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Modelscited by 22
  30. 2025LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenescited by 19
  31. 2025QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Predictioncited by 19
  32. 2021MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Datacited by 18
  33. 2023Benchmarking the Robustness of LiDAR Semantic Segmentation Modelscited by 18
  34. 2023ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understandingcited by 16
  35. 2022CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentationcited by 15
  36. 2022Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learningcited by 15
  37. 2024LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernelscited by 15
  38. 2024Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understandingcited by 15

50 most cited of 87 All 87 (JSON)