Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method

• URL: http://arxiv.org/abs/2308.10003v1
• Date: Sat, 19 Aug 2023 12:48:10 GMT
• Title: Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method
• Authors: Xiangyang Zhu, Yiling Pan, Bailin Deng and Bin Wang
• Abstract summary: We introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene. Our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient.
• Score: 19.330797817738542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. Our method follows an analysis-by-synthesis approach and consists of two phases. In the initialization phase, we use traditional SfM and MVS methods to reconstruct a virtual scene roughly matching the real scene. Then in the optimization phase, we adopt a hybrid approach to refine the geometry and reflectance, where the geometry is first optimized using an approximate differentiable rendering method, and the reflectance is optimized afterward using a physically-based differentiable rendering method. Our hybrid approach combines the efficiency of approximate methods with the high-quality results of physically-based methods. Extensive experiments on synthetic and real data demonstrate that our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient.

Related papers

• Wonder3D: Single Image to 3D using Cross-Domain Diffusion [105.16622018766236]
  Wonder3D is a novel method for efficiently generating high-fidelity textured meshes from single-view images. To holistically improve the quality, consistency, and efficiency of image-to-3D tasks, we propose a cross-domain diffusion model.
  arXiv  Detail & Related papers  (2023-10-23T15:02:23Z)
• High-Fidelity Clothed Avatar Reconstruction from a Single Image [73.15939963381906]
  We propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction from a single image. We use an implicit model to learn the general shape in the canonical space of a person in a learning-based way. We refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way.
  arXiv  Detail & Related papers  (2023-04-08T04:01:04Z)
• Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering [22.2095090385119]
  Given an imperfect reconstructed 3D model, most previous methods have focused on the refinement of either geometry, texture, or camera pose. We propose a novel optimization approach based on differentiable rendering, which integrates the optimization of camera pose, geometry, and texture into a unified framework. Using differentiable rendering, an image-level adversarial loss is applied to further improve the 3D model, making it more photorealistic.
  arXiv  Detail & Related papers  (2022-08-15T04:32:41Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• Implicit Neural Deformation for Multi-View Face Reconstruction [43.88676778013593]
  We present a new method for 3D face reconstruction from multi-view RGB images. Unlike previous methods which are built upon 3D morphable models, our method leverages an implicit representation to encode rich geometric features. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-05T07:02:53Z)
• Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
  We present an efficient method for joint optimization of materials and lighting from multi-view image observations. We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
  arXiv  Detail & Related papers  (2021-11-24T13:58:20Z)
• Shape and Reflectance Reconstruction in Uncontrolled Environments by Differentiable Rendering [27.41344744849205]
  We propose an efficient method to reconstruct the scene's 3D geometry and reflectance from multi-view photography using conventional hand-held cameras. Our method also shows superior performance compared to state-of-the-art alternatives in novel view visually synthesis and quantitatively.
  arXiv  Detail & Related papers  (2021-10-25T14:09:10Z)
• Inverting Generative Adversarial Renderer for Face Reconstruction [58.45125455811038]
  In this work, we introduce a novel Generative Adversa Renderer (GAR) GAR learns to model the complicated real-world image, instead of relying on the graphics rules, it is capable of producing realistic images. Our method achieves state-of-the-art performances on multiple face reconstruction.
  arXiv  Detail & Related papers  (2021-05-06T04:16:06Z)
• Efficient and Differentiable Shadow Computation for Inverse Problems [64.70468076488419]
  Differentiable geometric computation has received increasing interest for image-based inverse problems. We propose an efficient yet efficient approach for differentiable visibility and soft shadow computation. As our formulation is differentiable, it can be used to solve inverse problems such as texture, illumination, rigid pose, and deformation recovery from images.
  arXiv  Detail & Related papers  (2021-04-01T09:29:05Z)
• Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering [20.68222611798537]
  We introduce a new analysis-by-synthesis technique capable of producing high-quality reconstructions. Unlike most previous methods that handle geometry and reflectance largely separately, our method unifies the optimization of both. To obtain physically accurate gradient estimates, we develop a new GPU-based Monte Carlo differentiable rendering theory.
  arXiv  Detail & Related papers  (2021-03-28T19:44:05Z)

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.