No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images

• URL: http://arxiv.org/abs/2410.24207v1
• Date: Thu, 31 Oct 2024 17:58:22 GMT
• Title: No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images
• Authors: Botao Ye, Sifei Liu, Haofei Xu, Xueting Li, Marc Pollefeys, Ming-Hsuan Yang, Songyou Peng,
• Abstract summary: NoPoSplat is a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from multi-view images. Our model achieves real-time 3D Gaussian reconstruction during inference. This work makes significant advances in pose-free generalizable 3D reconstruction and demonstrates its applicability to real-world scenarios.
• Score: 100.80376573969045
• License:
• Abstract: We introduce NoPoSplat, a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from \textit{unposed} sparse multi-view images. Our model, trained exclusively with photometric loss, achieves real-time 3D Gaussian reconstruction during inference. To eliminate the need for accurate pose input during reconstruction, we anchor one input view's local camera coordinates as the canonical space and train the network to predict Gaussian primitives for all views within this space. This approach obviates the need to transform Gaussian primitives from local coordinates into a global coordinate system, thus avoiding errors associated with per-frame Gaussians and pose estimation. To resolve scale ambiguity, we design and compare various intrinsic embedding methods, ultimately opting to convert camera intrinsics into a token embedding and concatenate it with image tokens as input to the model, enabling accurate scene scale prediction. We utilize the reconstructed 3D Gaussians for novel view synthesis and pose estimation tasks and propose a two-stage coarse-to-fine pipeline for accurate pose estimation. Experimental results demonstrate that our pose-free approach can achieve superior novel view synthesis quality compared to pose-required methods, particularly in scenarios with limited input image overlap. For pose estimation, our method, trained without ground truth depth or explicit matching loss, significantly outperforms the state-of-the-art methods with substantial improvements. This work makes significant advances in pose-free generalizable 3D reconstruction and demonstrates its applicability to real-world scenarios. Code and trained models are available at https://noposplat.github.io/.

Related papers

• PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence [3.61512056914095]
  We present PreF3R, Pose-Free Feed-forward 3D Reconstruction from an image sequence of variable length. PreF3R removes the need for camera calibration and reconstructs the 3D Gaussian field within a canonical coordinate frame directly from a sequence of unposed images.
  arXiv  Detail & Related papers  (2024-11-25T19:16:29Z)
• NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model [57.92709692193132]
  NovelGS is a diffusion model for Gaussian Splatting given sparse-view images. We leverage the novel view denoising through a transformer-based network to generate 3D Gaussians.
  arXiv  Detail & Related papers  (2024-11-25T07:57:17Z)
• GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views [67.34073368933814]
  We propose a generalizable Gaussian Splatting approach for high-resolution image rendering under a sparse-view camera setting. We train our Gaussian parameter regression module on human-only data or human-scene data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. Experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
  arXiv  Detail & Related papers  (2024-11-18T08:18:44Z)
• USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting [45.246178004823534]
  Spike cameras, as an innovative neuromorphic camera that captures scenes with the 0-1 bit stream at 40 kHz, are increasingly employed for the 3D reconstruction task. Previous spike-based 3D reconstruction approaches often employ a casecased pipeline. We propose a synergistic optimization framework, textbfUSP-Gaussian, that unifies spike-based image reconstruction, pose correction, and Gaussian splatting into an end-to-end framework.
  arXiv  Detail & Related papers  (2024-11-15T14:15:16Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose [44.13819148680788]
  We develop a novel construct-and-optimize method for sparse view synthesis without camera poses. Specifically, we construct a solution by using monocular depth and projecting pixels back into the 3D world. We demonstrate results on the Tanks and Temples and Static Hikes datasets with as few as three widely-spaced views.
  arXiv  Detail & Related papers  (2024-05-06T17:36:44Z)
• iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching [14.737266480464156]
  We present a method named iComMa to address the 6D camera pose estimation problem in computer vision. We propose an efficient method for accurate camera pose estimation by inverting 3D Gaussian Splatting (3DGS)
  arXiv  Detail & Related papers  (2023-12-14T15:31:33Z)
• GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
  We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
  arXiv  Detail & Related papers  (2023-12-04T18:59:55Z)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)

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.