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arXiv Computer Vision

Camera Splatting for Continuous View Optimization

The paper introduces Camera Splatting, a novel framework for optimizing camera viewpoints in novel view synthesis. Each camera is represented as a 3D Gaussian (camera splat), and virtual point cameras are positioned near the surface to sample the distribution of these splats. By continuously refining the camera splats to match desired target distributions observed from the point cameras, the method achieves better capture of complex view‑dependent effects such as metallic reflections and detailed textures compared to the Farthest View Sampling approach.

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arXiv Computer Vision
5d ago

RGS: Reflection-aware Gaussian Splatting via Learning Geometry Continuity for Reflective Objects

The paper introduces Reflection-aware Gaussian Splatting (RGS), a physically-based deferred rendering framework that improves novel view synthesis for reflective objects. RGS leverages a powerful 3D foundation model to provide a strong geometric prior and employs cross-view shape consistency regularization to prevent surface collapse and reduce geometric hollows. Additionally, a reflection-aware densification strategy captures specular variations across views, resulting in higher-quality renderings of reflective objects.

By Xiaobiao Du, Yida Wang, Cheng Bi, Kun Zhan, Xin Yu
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arXiv Computer Vision
Sep 3

InceptionGS: Generative Bootstrapping for Large-Scale Gaussian Splatting under Unstructured View Sampling

InceptionGS is a method that improves large‑scale Gaussian splatting for scenes captured with unstructured view sampling. It starts from an initial Gaussian splatting and selectively repairs areas affected by sparse views by integrating scene‑ and view‑adaptive generative priors, while keeping well‑covered regions unchanged. Experiments on real‑world scenes show that this hybrid reconstruction‑generation approach yields higher‑fidelity results than existing methods.

By Tianheng Lu, Guangyu Wang, Ruqi Huang, Lu Fang
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Hugging Face Trending Papers
Jul 20

Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) employs Gaussian primitives for explicit scene representation, facilitating real-time, high-fidelity reconstruction and novel view synthesis of complex scenes. However, the explicit modeling inherent in 3DGS introduces a gradient bias during optimization, rendering its non-convex optimization process highly susceptible to convergence toward local suboptimal solutions.

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