Multi-View Stereo on Yida Wang

MVGS: Multi-View Regulated Gaussian Splatting for Novel View Synthesis

Wed, 24 Jun 2026 00:00:00 +0000

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Abstract

Recent works in novel view synthesis, \textit{e.g.}, Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS), have significantly advanced rendering quality and efficiency. However, existing Gaussian-based novel view synthesis methods typically follow a single-view optimization paradigm. We observed that this optimization paradigm suffers from unstable gradients, leading to suboptimal rendering quality. To tackle this issue, we present a novel multi-view regulated Gaussian Splatting (MVGS) that fully leverages a multi-view coherent (MVC) constraint throughout the optimization process. Specifically, our proposed MVC enhances 3D Gaussian multi-view consistency and thus ensures smoother gradient updates. Furthermore, since single-scale training usually leads to suboptimal solutions, we propose a cross-intrinsic guidance scheme in a coarse-to-fine manner to further improve the convergence of multi-view optimization in 3DGS. In particular, by incorporating more multi-view images at the low resolution, we can optimize 3D Gaussians with a more comprehensive perspective. Then, finer-scale Gaussians are initialized by coarsely estimated ones instead of optimizing full-scale 3D Gaussians from scratch. Moreover, we found that 3D Gaussians usually struggle to fit 2D training views with minimal overlap. Thus, we propose a novel multi-view cross-ray densification strategy, where 3D Gaussians are dynamically split to accommodate drastic viewpoint variations in the multi-view optimization process. In this way, the multi-view consistency can be further improved. Notably, our proposed MVGS method is a plug-and-play optimizer. Extensive experiments across various tasks demonstrate that our proposed MVGS improves existing Gaussian-based methods and achieves state-of-the-art performance.

StreetForward: Perceiving Dynamic Street with Feedforward Causal Attention

Wed, 22 Apr 2026 10:07:05 +0000

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We present StreetForward, a pose-free and tracker-free feedforward framework for dynamic street reconstruction. Building upon alternating attention, it introduces a temporal mask attention module that captures dynamic motion from image sequences and produces motion-aware latent representations. Static content and dynamic instances are represented uniformly with 3D Gaussian Splatting and optimized jointly through cross-frame rendering with spatio-temporal consistency, enabling high-fidelity novel-view synthesis at new poses and times while also estimating per-pixel velocities.

Ray-adaptive Neural Surface Reconstruction (RaNeuS)

Sun, 07 Apr 2024 10:15:01 +0200

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Our objective is to leverage a differentiable radiance field e.g. NeRF to reconstruct detailed 3D surfaces in addition to producing the standard novel view renderings. RaNeuS adaptively adjusts the regularization on the signed distance field so that unsatisfying rendering rays won’t enforce strong Eikonal regularization which is ineffective, and allow the gradients from regions with well-learned radiance to effectively back-propagated to the SDF. Consequently, balancing the two objectives in order to generate accurate and detailed surfaces.

Rendering, Animating and Meshing Actors with NeRF

Wed, 30 Nov 2022 10:15:01 +0200

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A library for rendering neural actors, and benchmarking dynamic NeRF

Cite

If you find this work useful in your research, please cite:

@misc{rama2023wang,
Author = {Yida Wang},
Year = {2023},
Note = {https://github.com/wangyida/neural-actor},
Title = {Rendering, Animating and Meshing Actors with NeRF}
}