LiftRefine: Progressively Refined View Synthesis from 3D Lifting with Volume-Triplane Representations

• URL: http://arxiv.org/abs/2412.14464v1
• Date: Thu, 19 Dec 2024 02:23:55 GMT
• Title: LiftRefine: Progressively Refined View Synthesis from 3D Lifting with Volume-Triplane Representations
• Authors: Tung Do, Thuan Hoang Nguyen, Anh Tuan Tran, Rang Nguyen, Binh-Son Hua,
• Abstract summary: We propose a new view synthesis method via a 3D neural field from both single or few-view input images. Our reconstruction model first lifts one or more input images to the 3D space from a volume as the coarse-scale 3D representation. Our diffusion model then hallucinates missing details in the rendered images from tri-planes.
• Score: 21.183524347952762
• License:
• Abstract: We propose a new view synthesis method via synthesizing a 3D neural field from both single or few-view input images. To address the ill-posed nature of the image-to-3D generation problem, we devise a two-stage method that involves a reconstruction model and a diffusion model for view synthesis. Our reconstruction model first lifts one or more input images to the 3D space from a volume as the coarse-scale 3D representation followed by a tri-plane as the fine-scale 3D representation. To mitigate the ambiguity in occluded regions, our diffusion model then hallucinates missing details in the rendered images from tri-planes. We then introduce a new progressive refinement technique that iteratively applies the reconstruction and diffusion model to gradually synthesize novel views, boosting the overall quality of the 3D representations and their rendering. Empirical evaluation demonstrates the superiority of our method over state-of-the-art methods on the synthetic SRN-Car dataset, the in-the-wild CO3D dataset, and large-scale Objaverse dataset while achieving both sampling efficacy and multi-view consistency.

Related papers

• DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models [67.50989119438508]
  We introduce DSplats, a novel method that directly denoises multiview images using Gaussian-based Reconstructors to produce realistic 3D assets. Our experiments demonstrate that DSplats not only produces high-quality, spatially consistent outputs, but also sets a new standard in single-image to 3D reconstruction.
  arXiv  Detail & Related papers  (2024-12-11T07:32:17Z)
• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation [54.27399121779011]
  We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. We show that our approach can yield more accurate synthesis compared to recent state-of-the-art, including distillation-based 3D inference and prior multi-view generation methods.
  arXiv  Detail & Related papers  (2024-04-04T17:59:57Z)
• Denoising Diffusion via Image-Based Rendering [54.20828696348574]
  We introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images.
  arXiv  Detail & Related papers  (2024-02-05T19:00:45Z)
• CAD: Photorealistic 3D Generation via Adversarial Distillation [28.07049413820128]
  We propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Our method unlocks the generation of high-fidelity and photorealistic 3D content conditioned on a single image and prompt.
  arXiv  Detail & Related papers  (2023-12-11T18:59:58Z)
• Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views [47.215089338101066]
  We present Sparse3D, a novel 3D reconstruction method tailored for sparse view inputs. Our approach distills robust priors from a multiview-consistent diffusion model to refine a neural radiance field. By tapping into 2D priors from powerful image diffusion models, our integrated model consistently delivers high-quality results.
  arXiv  Detail & Related papers  (2023-08-27T11:52:00Z)
• IT3D: Improved Text-to-3D Generation with Explicit View Synthesis [71.68595192524843]
  This study presents a novel strategy that leverages explicitly synthesized multi-view images to address these issues. Our approach involves the utilization of image-to-image pipelines, empowered by LDMs, to generate posed high-quality images. For the incorporated discriminator, the synthesized multi-view images are considered real data, while the renderings of the optimized 3D models function as fake data.
  arXiv  Detail & Related papers  (2023-08-22T14:39:17Z)
• Generative Novel View Synthesis with 3D-Aware Diffusion Models [96.78397108732233]
  We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image. Our method makes use of existing 2D diffusion backbones but, crucially, incorporates geometry priors in the form of a 3D feature volume. In addition to generating novel views, our method has the ability to autoregressively synthesize 3D-consistent sequences.
  arXiv  Detail & Related papers  (2023-04-05T17:15:47Z)
• GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis [43.4859484191223]
  We propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene. By introducing a multi-scale patch-based discriminator, we demonstrate synthesis of high-resolution images while training our model from unposed 2D images alone.
  arXiv  Detail & Related papers  (2020-07-05T20:37:39Z)

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.