mlsanityData to 7 Oct 2026

Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

arXiv 2209.10663 ↗First posted 21 Sep 2022cs.RO cs.CVcited by 8

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Abstract (arXiv)

Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (ConvBKI) layer which learns to perform explicit Bayesian inference within a depthwise separable convolution layer to maximize efficency while maintaining reliability simultaneously. We apply our layer to the task of real-time 3D semantic mapping, where we learn semantic-geometric probability distributions for LiDAR sensor information and incorporate semantic predictions into a global map. We evaluate our network against state-of-the-art semantic mapping algorithms on the KITTI data set, demonstrating improved latency with comparable semantic label inference results.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 46 references that are in the map. First posted on arXiv 21 Sep 2022.

Builds on

26 of 46 references.

References · 26

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14 more
  1. 2016SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networkscited by 134
  2. 2020MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Mapscited by 100
  3. 2021SuMa++: Efficient LiDAR-based Semantic SLAMcited by 100
  4. 2020Semantic MapNet: Building Allocentric Semantic Maps and Representations from Egocentric Viewscited by 57
  5. 2022MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environmentscited by 40
  6. 2021Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistencycited by 38
  7. 2019Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mappingcited by 34
  8. 2017DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networkscited by 17
  9. 2018Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Datacited by 16
  10. 2017Semantic 3D Occupancy Mapping through Efficient High Order CRFscited by 15
  11. 2021NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mappingcited by 14
  12. 2021SemSegMap- 3D Segment-Based Semantic Localizationcited by 10
  13. 2017Gaussian Processes Semantic Map Representationcited by 6
  14. 2021Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inferencecited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 6.

8 papers.

  1. 2024CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillationcited by 27
  2. 2024Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splattingcited by 12
  3. 2023ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertaintycited by 10
  4. 2024LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detectioncited by 10
  5. 2024UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimationcited by 5
  6. 2024Uncertainty-aware Semantic Mapping in Off-road Environments with Dempster-Shafer Theory of Evidencecited by 2
  7. 2024LatentBKI: Open-Dictionary Continuous Mapping in Visual-Language Latent Spaces with Quantifiable Uncertaintycited by 2
  8. 2023Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAMcited by 0