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Remote Sensing Sparse-View 3D Gaussian Splatting via Depth Image-Based Rendering

Paper recorded by Signals 4 on 2026-09-28 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29

Category: cs.CV · 计算机视觉 · first seen 2026-09-29

Abstract

Remote sensing novel view synthesis under sparse observations remains challenging due to insufficient geometric constraints and limited cross-view supervision. Existing Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) methods are prone to overfitting and face challenges of depth ambiguities, missing cross-view information, and insufficient constraints in under-observed regions. To ad

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#16 most recent of 342 cs.CV papers we have recorded · ↑ newer: EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversa · ↓ older: FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Genera
Cite this page: Remote Sensing Sparse-View 3D Gaussian Splatting via Depth Image-Based Rendering: the #16 most recent of 342 cs.CV papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/remote-sensing-sparse-view-3d-gaussian-splatting-via-depth-image-based-rendering.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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