Reconstructing 3D scenes from multi-view image sequences remains a significant challenge in practical applications. While recent advances in 3D Gaussian Splatting have enabled high-quality rendering, existing methods rely heavily on pixel-level L1 loss, which misaligns with human perception, leading to a lack of high-frequency details and the emergence of artifacts. Additionally, the position gradient-based densification strategy often results in under-densified Gaussian primitives, thereby degrading rendering quality. To address these challenges, we propose Pano-GS, a perception-aware Gaussian optimization framework. Specifically, we introduce a gradient consistency-constrained loss to capture high-frequency details, mitigating the inherent shortcomings of traditional L1 loss and enhancing reconstruction fidelity. In addition, we use a multi-criteria densification strategy to reduce the sole reliance on average position gradients. Extensive experiments demonstrate that Pano-GS achieves state-of-the-art performance, confirming its effectiveness and robust generalization across diverse real-world scenes.

Recently proposed 3D Gaussian Splatting (3DGS) has achieved state-of-the-art results in the fields of novel view synthesis, but it heavily relies on pre-computed camera poses. Although recent methods mitigate by leveraging explicit representations achieve novel view synthesis without requiring camera poses, Gaussians lack an appropriate growth strategy and accurate geometric information, which may lead to noticeable artifacts and distortions, particularly in complex scenes with large camera movements. To address these issues, we propose ADC-GS, where we introduce an Adaptive Depth Alignment (ADA) strategy to utilize monocular depth information from adjacent frames as guidance for camera pose optimization and provides geometric supervision through depth consistency for training global Gaussians. Furthermore, we demonstrate the drawbacks of the progressive growth strategy in Gaussians and further enhance reconstruction quality by refining the growth strategy. Extensive experiments on the challenging Tanks and Temples dataset show that our method achieves state-of-the-art results in both novel view synthesis quality and pose estimation accuracy.

Recently, 3D Gaussian Splatting (3D-GS) has demonstrated impressive results in novel view synthesis, achieving outstanding rendering quality and speed. However, 3D-GS heavily relies on the quality of the initial point cloud, and its original Adaptive Density Control (ADC) module has difficulty handling regions with sparse initial points. This issue arises because larger Gaussians typically result in lower opacity under the current ADC process. Consequently, multiple Gaussian primitives tend to overlap, leading to redundant coverage of the same spatial region. To address this problem, we propose HDA-GS, which effectively increases point density in the regions of sparse initial point cloud. Simultaneously, we introduce anisotropic opacity to enhance the ability to represent details in images under complex environments. Our method provides a plug-and-play solution that can be seamlessly incorporated into existing 3D-GS techniques. Extensive qualitative and quantitative experiments on challenging datasets, including Mip-NeRF360, Tanks&Temples, and DeepBlending, confirm that our method achieves state-of-the-art rendering quality while maintaining real-time performance.

3D Gaussian Splatting (3DGS) has recently made breakthrough progress in radiance field reconstruction but struggles with multi-view inconsistency. Modern cameras often apply tailored enhancements to each view when capturing multi-view images. While this improves individual image quality, it inevitably disrupts the multi-view consistency assumption, particularly in challenging conditions like nighttime scenes. This disrupts 3DGS’s ability to accurately reconstruct the scene, resulting in the emergence of artifacts such as "floaters". To tackle this challenge, we propose equipping each training view with a 4D bilateral affine grid to decouple the inconsistencies across multi-view images, guiding more accurate 3DGS reconstruction. To solve the problem of less visually appealing renderings caused by the absence of certain enhancements, our 4D bilateral affine grid also serves a dual purpose: it propagates enhanced features from a single edited image throughout the entire 3D scene, allowing for comprehensive 3D editing with just one image. Experiments demonstrate that our method not only effectively decouples inconsistencies in multi-view images, enabling more precise 3DGS reconstruction, but also facilitates refined 3D editing, further improving the visual quality of otherwise monotonous scenes.

Recently, 3D Gaussian Splatting (3DGS) has achieved state-of-the-art rendering results. However, its efficiency relies on simplifications that disregard the thickness of Gaussian primitives and their overlapping interactions. These simplifications can lead to popping artifacts due to inaccurate sorting, thereby affecting the rendering quality. In this paper, we propose Screen-Aligned Primitives (SAP), an anisotropic kernel that generates primitives parallel to the image plane for each view. Our rasterization pipeline enables full per-pixel ordering in real time. Since the primitives are parallel for a given viewpoint, a single global sorting operation suffices for correct per-pixel depth ordering. We formulate 3D reconstruction as a combination of a 3D-consistent decoder and 2D view-specific primitives, and further propose a highly efficient decoder to ensure 3D consistency. Moreover, within our framework, the primitive function values remain consistent between view space and screen space, allowing arbitrary radial basis functions (RBFs) to represent the scene without introducing projection errors. Experiments on diverse datasets demonstrate that our method achieves state-of-the-art rendering quality while maintaining real-time performance.