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.

Due to the complex and highly dynamic motions in the real world, synthesizing dynamic videos from multi-view inputs for arbitrary viewpoints is challenging. Previous works based on neural radiance field or 3D Gaussian splatting are limited to modeling fine-scale motion, greatly restricting their application. In this paper, we introduce LocalDyGS, which consists of two parts to adapt our method to both large-scale and fine-scale motion scenes: 1) We decompose a complex dynamic scene into streamlined local spaces defined by seeds, enabling global modeling by capturing motion within each local space. 2) We decouple static and dynamic features for local space motion modeling. A static feature shared across time steps captures static information, while a dynamic residual field provides time-specific features. These are combined and decoded to generate Temporal Gaussians, modeling motion within each local space. As a result, we propose a novel dynamic scene reconstruction framework to model highly dynamic real-world scenes more realistically. Our method not only demonstrates competitive performance on various fine-scale datasets compared to state-of-the-art (SOTA) methods, but also represents the first attempt to model larger and more complex highly dynamic scenes. Project page: https://wujh2001.github.io/LocalDyGS/.