READ: Large-Scale Neural Scene Rendering for Autonomous Driving

• URL: http://arxiv.org/abs/2205.05509v1
• Date: Wed, 11 May 2022 14:02:14 GMT
• Title: READ: Large-Scale Neural Scene Rendering for Autonomous Driving
• Authors: Zhuopeng Li, Lu Li, Zeyu Ma, Ping Zhang, Junbo Chen, Jianke Zhu
• Abstract summary: A large-scale neural rendering method is proposed to synthesize the autonomous driving scene. Our model can not only synthesize realistic driving scenes but also stitch and edit driving scenes.
• Score: 21.144110676687667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthesizing free-view photo-realistic images is an important task in multimedia. With the development of advanced driver assistance systems~(ADAS) and their applications in autonomous vehicles, experimenting with different scenarios becomes a challenge. Although the photo-realistic street scenes can be synthesized by image-to-image translation methods, which cannot produce coherent scenes due to the lack of 3D information. In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to synthesize large-scale driving scenarios on a PC through a variety of sampling schemes. In order to represent driving scenarios, we propose an {\omega} rendering network to learn neural descriptors from sparse point clouds. Our model can not only synthesize realistic driving scenes but also stitch and edit driving scenes. Experiments show that our model performs well in large-scale driving scenarios.

Related papers

• FreeVS: Generative View Synthesis on Free Driving Trajectory [55.49370963413221]
  FreeVS is a novel fully generative approach that can synthesize camera views on free new trajectories in real driving scenes. FreeVS can be applied to any validation sequences without reconstruction process and synthesis views on novel trajectories.
  arXiv  Detail & Related papers  (2024-10-23T17:59:11Z)
• Learning autonomous driving from aerial imagery [67.06858775696453]
  Photogrammetric simulators allow the synthesis of novel views through the transformation of pre-generated assets into novel views. We use a Neural Radiance Field (NeRF) as an intermediate representation to synthesize novel views from the point of view of a ground vehicle.
  arXiv  Detail & Related papers  (2024-10-18T05:09:07Z)
• OmniRe: Omni Urban Scene Reconstruction [78.99262488964423]
  We introduce OmniRe, a holistic approach for efficiently reconstructing high-fidelity dynamic urban scenes from on-device logs. We propose a comprehensive 3DGS framework for driving scenes, named OmniRe, that allows for accurate, full-length reconstruction of diverse dynamic objects in a driving log.
  arXiv  Detail & Related papers  (2024-08-29T17:56:33Z)
• AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction [17.600027937450342]
  AutoSplat is a framework employing Gaussian splatting to achieve highly realistic reconstructions of autonomous driving scenes. Our method enables multi-view consistent simulation of challenging scenarios including lane changes.
  arXiv  Detail & Related papers  (2024-07-02T18:36:50Z)
• Urban Scene Diffusion through Semantic Occupancy Map [49.20779809250597]
  UrbanDiffusion is a 3D diffusion model conditioned on a Bird's-Eye View (BEV) map. Our model learns the data distribution of scene-level structures within a latent space. After training on real-world driving datasets, our model can generate a wide range of diverse urban scenes.
  arXiv  Detail & Related papers  (2024-03-18T11:54:35Z)
• Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting [32.59889755381453]
  Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view of dynamic urban street scenes. We introduce Street Gaussians, a new explicit scene representation that tackles these limitations. The proposed method consistently outperforms state-of-the-art methods across all datasets.
  arXiv  Detail & Related papers  (2024-01-02T18:59:55Z)
• Street-View Image Generation from a Bird's-Eye View Layout [95.36869800896335]
  Bird's-Eye View (BEV) Perception has received increasing attention in recent years. Data-driven simulation for autonomous driving has been a focal point of recent research. We propose BEVGen, a conditional generative model that synthesizes realistic and spatially consistent surrounding images.
  arXiv  Detail & Related papers  (2023-01-11T18:39:34Z)
• SceneGen: Learning to Generate Realistic Traffic Scenes [92.98412203941912]
  We present SceneGen, a neural autoregressive model of traffic scenes that eschews the need for rules and distributions. We demonstrate SceneGen's ability to faithfully model distributions of real traffic scenes.
  arXiv  Detail & Related papers  (2021-01-16T22:51:43Z)
• Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering [0.0]
  We introduce a hybrid generative neural graphics pipeline for improving the visual fidelity of driving simulations. We form 2D semantic images from 3D scenery consisting of simple object models without textures. These semantic images are then converted into photorealistic RGB images with a state-of-the-art Generative Adrial Network (GAN) trained on real-world driving scenes.
  arXiv  Detail & Related papers  (2020-07-31T03:25:17Z)
• SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving [27.948417322786575]
  We present a simple yet effective approach to generate realistic scenario sensor data. Our approach uses texture-mapped surfels to efficiently reconstruct the scene from an initial vehicle pass or set of passes. We then leverage a SurfelGAN network to reconstruct realistic camera images for novel positions and orientations of the self-driving vehicle.
  arXiv  Detail & Related papers  (2020-05-08T04:01:14Z)

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.