mlsanityData to 7 Oct 2026

Dynamic LiDAR Re-simulation using Compositional Neural Fields

arXiv 2312.05247 ↗First posted 8 Dec 2023cs.CVcited by 22

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Abstract (arXiv)

We introduce DyNFL, a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes. DyNFL processes LiDAR measurements from dynamic environments, accompanied by bounding boxes of moving objects, to construct an editable neural field. This field, comprising separately reconstructed static background and dynamic objects, allows users to modify viewpoints, adjust object positions, and seamlessly add or remove objects in the re-simulated scene. A key innovation of our method is the neural field composition technique, which effectively integrates reconstructed neural assets from various scenes through a ray drop test, accounting for occlusions and transparent surfaces. Our evaluation with both synthetic and real-world environments demonstrates that DyNFL substantially improves dynamic scene LiDAR simulation, offering a combination of physical fidelity and flexible editing capabilities.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 57 of its 64 references that are in the map. First posted on arXiv 8 Dec 2023.

Builds on

57 of 64 references.

References · 57

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  4. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  5. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  6. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
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38 more
  1. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 750
  2. 2023K-Planes: Explicit Radiance Fields in Space, Time, and Appearancecited by 742
  3. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 732
  4. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  5. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  6. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
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  11. 2022MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructioncited by 467
  12. 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 464
  13. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  14. 2022NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstructioncited by 311
  15. 2020Neural Scene Graphs for Dynamic Scenescited by 298
  16. 2023UniSim: A Neural Closed-Loop Sensor Simulatorcited by 294
  17. 2021Urban Radiance Fieldscited by 263
  18. 2022Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representationcited by 243
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  20. 2023Robust Dynamic Radiance Fieldscited by 193
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  23. 2020LiDARsim: Realistic LiDAR Simulation by Leveraging the Real Worldcited by 137
  24. 2022D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Videocited by 136
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  26. 2021Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weathercited by 95
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  38. 2023LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fieldscited by 46

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 11; 2026: 4.

22 papers.

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  1. 2025Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Modelscited by 80
  2. 2024SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Drivingcited by 66
  3. 2024Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gapcited by 20
  4. 2024LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splattingcited by 15
  5. 2025RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenescited by 15
  6. 2025LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decouplingcited by 12
  7. 2024Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGBcited by 10
  8. 2024LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulationcited by 10
  9. 2025RealEngine: Simulating Autonomous Driving in Realistic Contextcited by 10
  10. 2024LiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene Reconstructioncited by 8
  11. 2026Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Modelscited by 6
  12. 2024LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenescited by 4
10 more
  1. 2025InvRGB+L: Inverse Rendering of Complex Scenes with Unified Color and LiDAR Reflectance Modelingcited by 4
  2. 2025SaLF: Sparse Local Fields for Multi-Sensor Rendering in Real-Timecited by 3
  3. 2025SimULi: Real-Time LiDAR and Camera Simulation with Unscented Transformscited by 3
  4. 2025LiDAR-GS++:Improving LiDAR Gaussian Reconstruction via Diffusion Priorscited by 2
  5. 2025Physically Based Neural LiDAR Resimulationcited by 1
  6. 2025FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splattingcited by 1
  7. 2026Unified Sensor Simulation for Autonomous Drivingcited by 1
  8. 2025Neural-MMGS: Multi-modal Neural Gaussian Splats for Large-Scale Scene Reconstructioncited by 0
  9. 2026DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenescited by 0
  10. 2026Resolving Mixed Single-Photon LiDAR Returns for Foreground-View and Hidden Scene Reconstructioncited by 0