Branches swaying in the breeze, flags rippling in the wind, and boats rocking on the water all show how aerodynamics shape natural motion – an effect crucial for realism in vision and graphics. In this paper, we present Gaussian Swaying, a surface-based framework for aerodynamic simulation using 3D Gaussians. Unlike mesh-based methods that require costly meshing, or particle-based approaches that rely on discrete positional data, Gaussian Swaying models surfaces continuously with 3D Gaussians, enabling efficient and fine-grained aerodynamic interaction. Our framework unifies simulation and rendering on the same representation: Gaussian patches, which support force computation for dynamics while simultaneously providing normals for lightweight shading. Comprehensive experiments on both synthetic and real-world datasets across multiple metrics demonstrate that Gaussian Swaying achieves state-of-the-art performance and efficiency, offering a scalable approach for realistic aerodynamic scene simulation.

Inherently equipped with arbitrary resolution and multi-view consistency, the Neural Radiance Field (NeRF) as an implicit scene representation has drawn extensive attention. While traditional NeRFs excel at novel view synthesis (NVS) under ideal conditions, they overlook the potential of learning consistent geometric representations across varying sight qualities. Current methods mainly focus on optimizing synthesis under clear visibility, which limits their effectiveness in downstream scene understanding tasks where robust geometry comprehension is crucial. In this paper, we propose a NVS pre-training technique named ShadowNeRF which firstly synthesizes degraded views with shadowed regions to challenge the model in inferring complete scene geometries. We then design a self-supervised sight recovery process with a two-stage unshadowing framework, which progressively recovers neighboring areas and reveals geometric properties of invisible regions. This pre-training strategy of degradation synthesis and recovery, when combined with taskspecific fine-tuning, enhances the understanding of underlying scene structure for the model and strengthens its ability to process scenes under varying sight conditions. Through extensive experiments, we demonstrate that our pre-training and finetuning pipeline significantly improves the model performances in semantic segmentation and 3D object detection, as well as the reconstruction quality of complex scenes.

Skins wrapping around our bodies, leathers covering over the sofa, sheet metal coating the car – it suggests that objects are enclosed by a series of continuous surfaces, which provides us with informative geometry prior for objectness deduction. In this paper, we propose Gaussian-Det which leverages Gaussian Splatting as surface representation for multi-view based 3D object detection. Unlike existing monocular or NeRF-based methods which depict the objects via discrete positional data, Gaussian-Det models the objects in a continuous manner by formulating the input Gaussians as feature descriptors on a mass of partial surfaces. Furthermore, to address the numerous outliers inherently introduced by Gaussian splatting, we accordingly devise a Closure Inferring Module (CIM) for the comprehensive surface-based objectness deduction. CIM firstly estimates the probabilistic feature residuals for partial surfaces given the underdetermined nature of Gaussian Splatting, which are then coalesced into a holistic representation on the overall surface closure of the object proposal. In this way, the surface information Gaussian-Det exploits serves as the prior on the quality and reliability of objectness and the information basis of proposal refinement. Experiments on both synthetic and real-world datasets demonstrate that Gaussian-Det outperforms various existing approaches, in terms of both average precision and recall.