HDRGS: High Dynamic Range Gaussian Splatting

• URL: http://arxiv.org/abs/2408.06543v3
• Date: Sun, 3 Nov 2024 11:15:53 GMT
• Title: HDRGS: High Dynamic Range Gaussian Splatting
• Authors: Jiahao Wu, Lu Xiao, Rui Peng, Kaiqiang Xiong, Ronggang Wang,
• Abstract summary: High Dynamic Range (GS) method enhances color dimensionality by luminance and uses an asymmetric grid for tone-mapping. Our method surpasses current state-of-the-art techniques in both synthetic and real-world scenarios.
• Score: 19.119572715951172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed substantial advancements in the field of 3D reconstruction from 2D images, particularly following the introduction of the neural radiance field (NeRF) technique. However, reconstructing a 3D high dynamic range (HDR) radiance field, which aligns more closely with real-world conditions, from 2D multi-exposure low dynamic range (LDR) images continues to pose significant challenges. Approaches to this issue fall into two categories: grid-based and implicit-based. Implicit methods, using multi-layer perceptrons (MLP), face inefficiencies, limited solvability, and overfitting risks. Conversely, grid-based methods require significant memory and struggle with image quality and long training times. In this paper, we introduce Gaussian Splatting-a recent, high-quality, real-time 3D reconstruction technique-into this domain. We further develop the High Dynamic Range Gaussian Splatting (HDR-GS) method, designed to address the aforementioned challenges. This method enhances color dimensionality by including luminance and uses an asymmetric grid for tone-mapping, swiftly and precisely converting pixel irradiance to color. Our approach improves HDR scene recovery accuracy and integrates a novel coarse-to-fine strategy to speed up model convergence, enhancing robustness against sparse viewpoints and exposure extremes, and preventing local optima. Extensive testing confirms that our method surpasses current state-of-the-art techniques in both synthetic and real-world scenarios.

Related papers

• Gaussian Splatting LK [0.11249583407496218]
  This paper investigates the potential of regularizing the native warp field within the dynamic Gaussian Splatting framework. We show that we can exploit knowledge innate to the forward warp field network to derive an analytical velocity field. This derived Lucas-Kanade style analytical regularization enables our method to achieve superior performance in reconstructing highly dynamic scenes.
  arXiv  Detail & Related papers  (2024-07-16T01:50:43Z)
• A Refined 3D Gaussian Representation for High-Quality Dynamic Scene Reconstruction [2.022451212187598]
  In recent years, Neural Radiance Fields (NeRF) has revolutionized three-dimensional (3D) reconstruction with its implicit representation. 3D Gaussian Splatting (3D-GS) has departed from the implicit representation of neural networks and instead directly represents scenes as point clouds with Gaussian-shaped distributions. This paper purposes a refined 3D Gaussian representation for high-quality dynamic scene reconstruction. Experimental results demonstrate that our method surpasses existing approaches in rendering quality and speed, while significantly reducing the memory usage associated with 3D-GS.
  arXiv  Detail & Related papers  (2024-05-28T07:12:22Z)
• 2D Gaussian Splatting for Geometrically Accurate Radiance Fields [50.056790168812114]
  3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images. We demonstrate that our differentiable terms allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering.
  arXiv  Detail & Related papers  (2024-03-26T17:21:24Z)
• BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling [32.493592776662005]
  We analyze the robustness of Gaussian-Splatting-based methods against various image blur. We propose Blur Agnostic Gaussian Splatting (BAGS) to address this issue. BAGS introduces additional 2D modeling capacities such that a 3D-consistent and high quality scene can be reconstructed despite image-wise blur.
  arXiv  Detail & Related papers  (2024-03-07T22:21:08Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Towards High-quality HDR Deghosting with Conditional Diffusion Models [88.83729417524823]
  High Dynamic Range (LDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. DNNs still generate ghosting artifacts when LDR images have saturation and large motion. We formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition.
  arXiv  Detail & Related papers  (2023-11-02T01:53:55Z)
• HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN [0.4606926019499777]
  This paper introduces HyperNeRFGAN, a Generative Adversarial Network (GAN) architecture employing a hypernetwork paradigm to transform a Gaussian noise into the weights of a NeRF architecture. Despite its notable simplicity in comparison to existing state-of-the-art alternatives, the proposed model demonstrates superior performance on a diverse range of image datasets.
  arXiv  Detail & Related papers  (2023-01-27T10:21:18Z)
• Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis [63.25919018001152]
  We propose a fast deformable radiance field method to handle dynamic scenes. Our method achieves comparable performance to D-NeRF using only 20 minutes for training.
  arXiv  Detail & Related papers  (2022-06-15T17:49:08Z)
• 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)
• HDR Reconstruction from Bracketed Exposures and Events [12.565039752529797]
  Reconstruction of high-quality HDR images is at the core of modern computational photography. We present a multi-modal end-to-end learning-based HDR imaging system that fuses bracketed images and event in the feature domain. Our framework exploits the higher temporal resolution of events by sub-sampling the input event streams using a sliding window.
  arXiv  Detail & Related papers  (2022-03-28T15:04:41Z)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)

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.