LANe: Lighting-Aware Neural Fields for Compositional Scene Synthesis

• URL: http://arxiv.org/abs/2304.03280v1
• Date: Thu, 6 Apr 2023 17:59:25 GMT
• Title: LANe: Lighting-Aware Neural Fields for Compositional Scene Synthesis
• Authors: Akshay Krishnan, Amit Raj, Xianling Zhang, Alexandra Carlson, Nathan Tseng, Sandhya Sridhar, Nikita Jaipuria, James Hays
• Abstract summary: We present Lighting-Aware Neural Field (LANe) for compositional synthesis of driving scenes. We learn a scene representation that disentangles the static background and transient elements into a world-NeRF and class-specific object-NeRFs. We demonstrate the performance of our model on a synthetic dataset of diverse lighting conditions rendered with the CARLA simulator.
• Score: 65.20672798704128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural fields have recently enjoyed great success in representing and rendering 3D scenes. However, most state-of-the-art implicit representations model static or dynamic scenes as a whole, with minor variations. Existing work on learning disentangled world and object neural fields do not consider the problem of composing objects into different world neural fields in a lighting-aware manner. We present Lighting-Aware Neural Field (LANe) for the compositional synthesis of driving scenes in a physically consistent manner. Specifically, we learn a scene representation that disentangles the static background and transient elements into a world-NeRF and class-specific object-NeRFs to allow compositional synthesis of multiple objects in the scene. Furthermore, we explicitly designed both the world and object models to handle lighting variation, which allows us to compose objects into scenes with spatially varying lighting. This is achieved by constructing a light field of the scene and using it in conjunction with a learned shader to modulate the appearance of the object NeRFs. We demonstrate the performance of our model on a synthetic dataset of diverse lighting conditions rendered with the CARLA simulator, as well as a novel real-world dataset of cars collected at different times of the day. Our approach shows that it outperforms state-of-the-art compositional scene synthesis on the challenging dataset setup, via composing object-NeRFs learned from one scene into an entirely different scene whilst still respecting the lighting variations in the novel scene. For more results, please visit our project website https://lane-composition.github.io/.

Related papers

• Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes [68.14127205949073]
  We propose a novel GlobalLocal training framework for synthesizing a 3D scene using object proxies. We show that using proxies allows a wide variety of editing options, such as adjusting the placement of each independent object. Our results show that Set-the-Scene offers a powerful solution for scene synthesis and manipulation.
  arXiv  Detail & Related papers  (2023-03-23T17:17:29Z)
• DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis [90.32352050266104]
  DisCoScene is a 3Daware generative model for high-quality and controllable scene synthesis. It disentangles the whole scene into object-centric generative fields by learning on only 2D images with the global-local discrimination. We demonstrate state-of-the-art performance on many scene datasets, including the challenging outdoor dataset.
  arXiv  Detail & Related papers  (2022-12-22T18:59:59Z)
• Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination [63.992213016011235]
  We propose a method for scene relighting under novel views by learning a neural precomputed radiance transfer function. Our method can be solely supervised on a set of real images of the scene under a single unknown lighting condition. Results show that the recovered disentanglement of scene parameters improves significantly over the current state of the art.
  arXiv  Detail & Related papers  (2022-07-27T16:07:48Z)
• Unsupervised Discovery and Composition of Object Light Fields [57.198174741004095]
  We propose to represent objects in an object-centric, compositional scene representation as light fields. We propose a novel light field compositor module that enables reconstructing the global light field from a set of object-centric light fields.
  arXiv  Detail & Related papers  (2022-05-08T17:50:35Z)
• Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering [42.37007176376849]
  We present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering for a clustered and real-world scene. To survive the training in heavily cluttered scenes, we propose a scene-guided training strategy to solve the 3D space ambiguity in the occluded regions and learn sharp boundaries for each object.
  arXiv  Detail & Related papers  (2021-09-04T11:37:18Z)
• Object-Centric Neural Scene Rendering [19.687759175741824]
  We present a method for composing photorealistic scenes from captured images of objects. Our work builds upon neural radiance fields (NeRFs), which implicitly model the volumetric density and directionally-emitted radiance of a scene. We learn object-centric neural scattering functions (OSFs), a representation that models per-object light transport implicitly using a lighting- and view-dependent neural network.
  arXiv  Detail & Related papers  (2020-12-15T18:55:02Z)
• GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields [45.21191307444531]
  Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. Our key hypothesis is that incorporating a compositional 3D scene representation into the generative model leads to more controllable image synthesis.
  arXiv  Detail & Related papers  (2020-11-24T14:14:15Z)
• Neural Scene Graphs for Dynamic Scenes [57.65413768984925]
  We present the first neural rendering method that decomposes dynamic scenes into scene graphs. We learn implicitly encoded scenes, combined with a jointly learned latent representation to describe objects with a single implicit function.
  arXiv  Detail & Related papers  (2020-11-20T12:37:10Z)

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.