3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis. However, this technique requires dense training views to accurately reconstruct 3D geometry. A limited number of input views will significantly degrade reconstruction quality, resulting in artifacts such as “floaters” and “background collapse” at unseen viewpoints. In this work, we introduce SparseGS, an efficient training pipeline designed to address the limitations of 3DGS in scenarios with sparse training views. SparseGS incorporates depth priors, novel depth rendering techniques, and a pruning heuristic to mitigate floater artifacts, alongside an Unseen Viewpoint Regularization module to alleviate background collapses. Our extensive evaluations on the Mip-NeRF360, LLFF, and DTU datasets demonstrate that SparseGS achieves highquality reconstruction in both unbounded and forwardfacing scenarios, with as few as 12 and 3 input images, respectively, while maintaining fast training and real-time rendering capabilities.

As a core step in structure-from-motion and SLAM, robust feature detection and description under challenging scenarios such as significant viewpoint changes remain unresolved despite their ubiquity. While recent works have identified the importance of local features in modeling geometric transformations, these methods fail to learn the visual cues present in long-range relationships. We present Robust Deformable Detector (RDD), a novel and robust keypoint detector/descriptor leveraging the deformable transformer, which captures global context and geometric invariance through deformable self-attention mechanisms. Specifically, we observed that deformable attention focuses on key locations, effectively reducing the search space complexity and modeling the geometric invariance. Furthermore, we collected an Air-to-Ground dataset for training in addition to the standard MegaDepth dataset. Our proposed method outperforms all state-of-the-art keypoint detection/description methods in sparse matching tasks and is also capable of semi-dense matching. To ensure comprehensive evaluation, we introduce two challenging benchmarks: one emphasizing large viewpoint and scale variations, and the other being an Air-to-Ground benchmark — an evaluation setting that has recently gaining popularity for 3D reconstruction across different altitudes. Project page: https://xtcpete.github.io/rdd/.

Recent advancements in text-guided 3D object generation using diffusion priors struggle with illumination inconsistencies when applied to scene editing tasks like object replacement or insertion. To address this, we propose an illumination-aware 3D scene editing pipeline for 3D Gaussian Splatting (3DGS). Our method leverages state-of-the-art 2D diffusion inpainting [56] to handle global illumination context effectively. Specifically, we identify representative anchor views that capture scene-wide illumination, inpaint them using 2D diffusion models, and integrate the results into a coarse-to-fine 3DGS optimization process. In the fine step, we introduce Depth-guided Inpainting Score Distillation Sampling (DI-SDS) to refine geometry and texture details, capitalizing on the diversity of 2D priors. Our approach achieves locally precise edits with globally consistent illumination, demonstrating robustness in real scenes with highlights and shadows. Comparisons show superior results over state-of-the-art text-to-3D editing methods. Project page: https://corneliushsiao.github.io/GSLE.html.