D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting

• URL: http://arxiv.org/abs/2608.01588v1
• Date: Mon, 03 Aug 2026 01:46:19 GMT
• Title: D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting
• Abstract summary: We propose a sparse-camera dynamic 4D Gaussian Splatting framework guided by dual-source depth priors.<n>Across all nine dataset--view settings, D$2$-4DGS achieves the highest PSNR, improving by 1.33 dB on average over the best competing method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time rendering. However, existing methods typically require dense multi-view videos for sufficient geometric constraints, making capture expensive and limiting sparse-camera deployment. Reducing input views lowers acquisition cost but weakens geometry supervision, often causing missing structures and floating Gaussians. Depth priors provide geometric cues, yet no single source offers both dense coverage and reliable geometry. Monocular depth provides dense structure but is scale-ambiguous and locally biased, whereas multi-view geometric depth provides incomplete anchors consistent with the reconstruction coordinate system. To exploit their complementarity, we propose D$^2$-4DGS, a sparse-camera dynamic 4D Gaussian Splatting framework guided by dual-source depth priors. We align monocular estimates with valid multi-view geometric depths and verify their consistency to identify reliable geometric anchors. These verified anchors support consistency-aware pruning and depth supervision, while verified geometric depths and aligned mono-only estimates provide candidate geometry for densification in under-reconstructed regions. Finally, RGB-D joint optimization improves appearance fidelity and geometric consistency under sparse-view supervision. Across all nine dataset--view settings, D$^2$-4DGS achieves the highest PSNR, improving by 1.33 dB on average over the best competing method in each setting.

Related papers

• Visual Geometry Foundation-Aware Gaussians for Single-Frame Surround-View Driving Reconstruction [44.54907293631917]
  Single-frame surround-view reconstruction faces severe geometric instability and rendering artifacts due to minimal inter-camera overlap.<n>We propose VGGD, a visual geometry foundation-aware 3D Gaussian Splatting framework for feed-forward surround-view driving reconstruction.
  arXiv  Detail & Related papers  (2026-08-11T09:04:33Z)
• Ground4D: Consistency-Aware 4D Reconstruction from Monocular Video [69.94904173314505]
  We propose Ground4D, a geometry-grounded framework built on two stages.<n>First, we reconstruct multi-view-consistent 3D geometry and camera poses from monocular video.<n>Second, we conduct geometry-consistency-aware refinement via dynamic Gaussian Splatting.
  arXiv  Detail & Related papers  (2026-06-27T09:27:42Z)
• IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation [76.36174247570716]
  We propose IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images.<n> IVGT learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions.<n>It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints.
  arXiv  Detail & Related papers  (2026-05-15T17:59:57Z)
• Geometric 4D Stitching for Grounded 4D Generation [51.62488402931672]
  We propose Geometric 4D Stitching, an efficient framework that identifies missing geometric regions and complements them with geometrically grounded 4D stitches.<n>Our method constructs 4D scene representations in under 10 minutes on a single NVIDIA GTX 5090 GPU per one-step scene expansion.
  arXiv  Detail & Related papers  (2026-05-11T04:53:33Z)
• Geometrically Consistent Multi-View Scene Generation from Freehand Sketches [58.98194920417429]
  Freehand sketches are the most geometrically impoverished input one could offer a multi-view generator.<n>We address three compounding challenges; absent training data, the need for geometric reasoning from distorted 2D input, and cross-view consistency.<n>Our framework synthesizes all views in a single denoising process without requiring reference images, iterative refinement, or per-scene optimisation.
  arXiv  Detail & Related papers  (2026-04-15T18:00:45Z)
• GGPT: Geometry Grounded Point Transformer [22.64445696362087]
  We introduce the Geometry-Grounded Point Transformer (GGPT), a framework that augments feed-forward reconstruction with reliable sparse geometric guidance.<n>Building on this foundation, we propose a geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision.
  arXiv  Detail & Related papers  (2026-03-11T18:00:04Z)
• GVGS: Gaussian Visibility-Aware Multi-View Geometry for Accurate Surface Reconstruction [15.170414649311441]
  3D Gaussian Splatting enables efficient optimization and high-quality rendering, yet accurate surface reconstruction remains challenging.<n>We introduce a visibility-aware multi-view geometric consistency constraint that aggregates the visibility of shared Gaussian primitives across views.<n>We also propose a progressive quadtree-calibrated Monocular depth constraint that performs block-wise affine calibration from coarse to fine spatial scales.
  arXiv  Detail & Related papers  (2026-01-28T07:48:51Z)
• Geometry-Consistent 4D Gaussian Splatting for Sparse-Input Dynamic View Synthesis [17.560425604804305]
  GC-4DGS is a novel framework that infuses geometric consistency into 4D Gaussian Splatting (4DGS)<n>This paper presents GC-4DGS, a novel framework that infuses geometric consistency into 4D Gaussian Splatting (4DGS)
  arXiv  Detail & Related papers  (2025-11-28T10:11:48Z)
• Dens3R: A Foundation Model for 3D Geometry Prediction [44.13431776180547]
  Dens3R is a 3D foundation model designed for joint geometric dense prediction.<n>By integrating image-pair matching features with intrinsic invariance modeling, Dens3R accurately regresses multiple geometric quantities.
  arXiv  Detail & Related papers  (2025-07-22T07:22:30Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)
• 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.