LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes

• URL: http://arxiv.org/abs/2412.00592v3
• Date: Sat, 24 May 2025 04:59:51 GMT
• Title: LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes
• Authors: Shing-Hei Ho, Bao Thach, Minghan Zhu,
• Abstract summary: We present LiDAR-EDIT, a novel paradigm for generating synthetic LiDAR data for autonomous driving.<n>Our framework edits real-world LiDAR scans by introducing new object layouts while preserving the realism of the background environment.<n>Compared to end-to-end frameworks that generate LiDAR point clouds from scratch, LiDAR-EDIT offers users full control over the object layout.
• Score: 1.249418440326334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present LiDAR-EDIT, a novel paradigm for generating synthetic LiDAR data for autonomous driving. Our framework edits real-world LiDAR scans by introducing new object layouts while preserving the realism of the background environment. Compared to end-to-end frameworks that generate LiDAR point clouds from scratch, LiDAR-EDIT offers users full control over the object layout, including the number, type, and pose of objects, while keeping most of the original real-world background. Our method also provides object labels for the generated data. Compared to novel view synthesis techniques, our framework allows for the creation of counterfactual scenarios with object layouts significantly different from the original real-world scene. LiDAR-EDIT uses spherical voxelization to enforce correct LiDAR projective geometry in the generated point clouds by construction. During object removal and insertion, generative models are employed to fill the unseen background and object parts that were occluded in the original real LiDAR scans. Experimental results demonstrate that our framework produces realistic LiDAR scans with practical value for downstream tasks.

Related papers

• LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequences [10.426609103049572]
  LiDARCrafter is a unified framework for 4D LiDAR generation and editing.<n>It achieves state-of-the-art performance in fidelity, controllability, and temporal consistency across all levels.<n>The code and benchmark are released to the community.
  arXiv  Detail & Related papers  (2025-08-05T17:59:56Z)
• La La LiDAR: Large-Scale Layout Generation from LiDAR Data [45.5317990948996]
  Controllable generation of realistic LiDAR scenes is crucial for applications such as autonomous driving and robotics.<n>We propose Large-scale Layout-guided LiDAR generation model ("La La LiDAR"), a novel layout-guided generative framework.<n>La La LiDAR achieves state-of-the-art performance in both LiDAR generation and downstream perception tasks.
  arXiv  Detail & Related papers  (2025-08-05T17:59:55Z)
• SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis [11.615282010184917]
  We propose SN-LiDAR, a method that jointly performs accurate semantic segmentation, high-quality geometric reconstruction, and realistic LiDAR synthesis.<n>Specifically, we employ a coarse-to-fine planar-grid feature representation to extract global features from multi-frame point clouds.<n>Experiments on Semantic KITTI and KITTI-360 demonstrate the superiority of SN-LiDAR in both semantic and geometric reconstruction.
  arXiv  Detail & Related papers  (2025-04-11T08:51:23Z)
• OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving [74.06413946934002]
  We introduce OLiDM, a novel framework capable of generating high-fidelity LiDAR data at both the object and the scene levels.<n>OLiDM consists of two pivotal components: the Object-Scene Progressive Generation (OPG) module and the Object Semantic Alignment (OSA) module.<n>OPG adapts to user-specific prompts to generate desired foreground objects, which are subsequently employed as conditions in scene generation.<n>OSA aims to rectify the misalignment between foreground objects and background scenes, enhancing the overall quality of the generated objects.
  arXiv  Detail & Related papers  (2024-12-23T02:43:29Z)
• LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation [31.79143254487969]
  LiDAR-RT is a novel framework that supports real-time, physically accurate LiDAR re-simulation for driving scenes.<n>Our primary contribution is the development of an efficient and effective rendering pipeline.<n>Our framework supports realistic rendering with flexible scene editing operations and various sensor configurations.
  arXiv  Detail & Related papers  (2024-12-19T18:58:36Z)
• LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting [50.808933338389686]
  We present LiDAR-GS, a real-time, high-fidelity re-simulation of LiDAR scans in public urban road scenes.<n>The method achieves state-of-the-art results in both rendering frame rate and quality on publically available large scene datasets.
  arXiv  Detail & Related papers  (2024-10-07T15:07:56Z)
• Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting [16.938779241290735]
  Reconstructing an object from photos and placing it virtually in a new environment goes beyond the standard novel view synthesis task. This work presents a real-world dataset for measuring the reconstruction and rendering of objects for relighting.
  arXiv  Detail & Related papers  (2024-01-17T11:02:52Z)
• ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction Environments [1.4070907500169874]
  This paper introduces a new semantic segmentation dataset specifically tailored for construction sites. The dataset is designed to enhance the training and evaluation of object detection models.
  arXiv  Detail & Related papers  (2023-12-27T10:49:19Z)
• Context-Aware Indoor Point Cloud Object Generation through User Instructions [6.398660996031915]
  We present a novel end-to-end multi-modal deep neural network capable of generating point cloud objects seamlessly integrated with their surroundings. Our model revolutionizes scene modification by enabling the creation of new environments with previously unseen object layouts.
  arXiv  Detail & Related papers  (2023-11-26T06:40:16Z)
• CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models [85.69959024572363]
  CustomNet is a novel object customization approach that explicitly incorporates 3D novel view synthesis capabilities into the object customization process. We introduce delicate designs to enable location control and flexible background control through textual descriptions or specific user-defined images. Our method facilitates zero-shot object customization without test-time optimization, offering simultaneous control over the viewpoints, location, and background.
  arXiv  Detail & Related papers  (2023-10-30T17:50:14Z)
• LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts [107.11267074981905]
  We propose a semantically controllable layout-AWare diffusion model, termed LAW-Diffusion. We show that LAW-Diffusion yields the state-of-the-art generative performance, especially with coherent object relations.
  arXiv  Detail & Related papers  (2023-08-13T08:06:18Z)
• Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection [45.102312149413855]
  We study the synthesis-based LiDAR data augmentation approach (so-called GT-Aug) which offers maxium controllability over generated data samples. We propose Real-Aug, a synthesis-based augmentation method which prioritizes on generating realistic LiDAR scans. We achieve a state-of-the-art 0.744 NDS and 0.702 mAP on nuScenes test set.
  arXiv  Detail & Related papers  (2023-05-22T09:24:55Z)
• Neural LiDAR Fields for Novel View Synthesis [80.45307792404685]
  We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements. NFL combines the rendering power of neural fields with a detailed, physically motivated model of the LiDAR sensing process. We show that the improved realism of the synthesized views narrows the domain gap to real scans and translates to better registration and semantic segmentation performance.
  arXiv  Detail & Related papers  (2023-05-02T17:55:38Z)
• LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields [112.62936571539232]
  We introduce a new task, novel view synthesis for LiDAR sensors. Traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views. We use a neural radiance field (NeRF) to facilitate the joint learning of geometry and the attributes of 3D points.
  arXiv  Detail & Related papers  (2023-04-20T15:44:37Z)
• Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models [55.04969603431266]
  This paper proposes a method for generating images of customized objects specified by users. The method is based on a general framework that bypasses the lengthy optimization required by previous approaches. We demonstrate through experiments that our proposed method is able to synthesize images with compelling output quality, appearance diversity, and object fidelity.
  arXiv  Detail & Related papers  (2023-04-05T17:59:32Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• Wide and Narrow: Video Prediction from Context and Motion [54.21624227408727]
  We propose a new framework to integrate these complementary attributes to predict complex pixel dynamics through deep networks. We present global context propagation networks that aggregate the non-local neighboring representations to preserve the contextual information over the past frames. We also devise local filter memory networks that generate adaptive filter kernels by storing the motion of moving objects in the memory.
  arXiv  Detail & Related papers  (2021-10-22T04:35:58Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets [103.54691385842314]
  We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes. Our goal is to make the dataset creation process widely accessible. This enables important applications in inverse rendering, scene understanding and robotics.
  arXiv  Detail & Related papers  (2020-07-25T06:48:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.