Recent advances in surface reconstruction for 3D Gaussian Splatting (3DGS) have enabled remarkable geometric accuracy. However, their performance degrades in photometrically ambiguous regions such as reflective and textureless surfaces, where unreliable cues disrupt photometric consistency and hinder accurate geometry estimation. Reflected light is often partially polarized in a manner that reveals surface orientation, making polarization an optical complement to photometric cues in resolving such ambiguities. Therefore, we propose PolarGS, an optics-aware extension of RGB-based 3DGS that leverages polarization as an optical prior to resolve photometric ambiguities and enhance reconstruction accuracy. Specifically, we introduce two complementary modules: a polarization-guided photometric correction strategy, which ensures photometric consistency by identifying reflective regions via the Degree of Linear Polarization (DoLP) and refining reflective Gaussians with Color Refinement Maps; and a polarization-enhanced Gaussian densification mechanism for textureless area geometry recovery, which integrates both Angle and Degree of Linear Polarization (A&DoLP) into a PatchMatch-based depth completion process. This enables the back-projection and fusion of new Gaussians, leading to a more complete reconstruction. PolarGS is framework-agnostic and achieves superior geometric accuracy compared to state-of-the-art methods.

Neural implicit methods have made remarkable progress in 3D reconstruction. However, previous methods often assume view-independent properties of target objects, which fails to accurately reconstruct objects with challenging characteristics, such as transparency and high reflectivity. To address this limitation, we propose a polarimetric implicit 3D reconstruction method that integrates geometric and polarization information, enabling the production of high-quality meshes in complex scenes. For high-fidelity surface reconstruction, we introduce a view-dependent physical representation that thoroughly analyzes the subtle physical properties of reflections. The reconstruction process is further enhanced by a simple yet effective view-dependent detection algorithm and optimized using the principles of ray tracing and polarization. Experimental results demonstrate the superior performance of the proposed method in both real and synthetic scenarios.

Federated learning is susceptible to model poisoning attacks, especially those meticulously crafted for servers. Traditional defense methods mainly focus on updating assessments or robust aggregation against manually crafted myopic attacks. When facing advanced attacks, their defense stability is notably insufficient. Therefore, it is imperative to develop adaptive defenses against such advanced poisoning attacks. We find that benign clients exhibit significantly higher data distribution stability than malicious clients in federated learning in both CV and NLP tasks. Therefore, the malicious clients can be recognized by observing the stability of their data distribution. In this paper, we propose AdaAggRL, an RL-based Adaptive Aggregation method, to defend against sophisticated poisoning attacks. Specifically, we first utilize distribution learning to simulate the clients' data distributions. Then, we use maximum mean discrepancy (MMD) to calculate the pairwise similarity of the current local model data distribution, its historical data distribution, and global model data distribution. Finally, we use policy learning to adaptively determine the aggregation weights based on the above similarities. Experiments on four real-world datasets demonstrate that the proposed defense model significantly outperforms widely adopted defense models for sophisticated attacks.

Monocular 3D Gaussian Splatting (3DGS) SLAM methods demonstrate outstanding performance in rapid dense 3D reconstruction. Yet former methods frequently exhibit suboptimal localization and mapping quality when processing indoor objects characterized by weak textures, dark colors, and high reflectivity (e.g., leather furniture), primarily due to insufficient surface feature information, even with the aid of depth sensors. To overcome these limitations, this work pioneers the integration of polarization information into the 3DGS SLAM framework. Specifically, we introduce a polarization integrated SLAM front-end that leverages the abundant planar features inherent in indoor environments. By incorporating a Chroma Boost mechanism, our approach effectively enhances the spectral multi-view consistency during the SLAM process, while the integration of a Gaussian-visible polarization difference improves the robustness of keyframe registration in low-texture scenarios. We further propose a flattened Gaussian regularization coupled with normal consistency constraints to capture the local geometric features of surfaces more accurately. Moreover, a novel integration of Pol-RGB hierarchical density plane segmentation and multi-scale plane self-constraint substantially enhances the quality of scene surface reconstruction, with further azimuth refinement achieved through the angle of linear polarization (AoLP). Extensive experiments demonstrate that, compared with previous SLAM methods, our approach significantly improves surface reconstruction quality.

Recent advances in 3D Gaussian Splatting (3DGS) have improved the visual fidelity of dynamic avatar reconstruction. However, existing methods often overlook the inherent chromatic similarity of human skin tones, leading to poor capture of intricate facial geometry under subtle appearance changes. This is caused by the affine approximation of Gaussian projection, which fails to be perspective-aware to depth-induced shear effects. To this end, we propose True-to-Geometry Avatar Dynamic Reconstruction (TGA), a perspective-aware 4D Gaussian avatar framework that sensitively captures fine-grained facial variations for accurate 3D geometry reconstruction. Specifically, to enable color-sensitive and geometry-consistent Gaussian representations under dynamic conditions, we introduce Perspective-Aware Gaussian Transformation that jointly models temporal deformations and spatial projection by integrating Jacobian-guided adaptive deformation into the homogeneous formulation. Furthermore, we develop Incremental BVH Tree Pivoting to enable fast frame-by-frame mesh extraction for 4D Gaussian representations. A dynamic Gaussian Bounding Volume Hierarchy (BVH) tree is used to model the topological relationships among points, where active ones are filtered out by BVH pivoting and subsequently re-triangulated for surface reconstruction. Extensive experiments demonstrate that TGA achieves superior geometric accuracy.

Image dehazing under harsh weather conditions remains a challenging and ill-posed problem. In addition, acquiring real-time haze-free counterparts of hazy images poses difficulties. Existing approaches commonly synthesize hazy data by relying on estimated depth information, which is prone to errors due to its physical unreliability. While generative networks can transfer some hazy features to clear images, the resulting hazy images still exhibit an artificial appearance. In this paper, we introduce polarization cues to propose a haze simulation strategy to synthesize hazy data, ensuring visually pleasing results that adhere to physical laws. Leveraging on the simulated Polar-Haze dataset, we present a polarization state attention dehazing network (PSADNet), which consists of a polarization extraction module and a polarization dehazing module. The proposed polarization extraction model incorporates an attention mechanism to capture high-level image features related to polarization and chromaticity. The polarization dehazing module utilizes these features derived from the polarization analysis to enhance image dehazing capabilities while preserving the accuracy of the polarization information. Promising results are observed in both qualitative and quantitative experiments, supporting the effectiveness of the proposed PSADNet and the validity of polarization-based haze simulation strategy.

The emergence of the single-chip polarized color sensor now allows for simultaneously capturing chromatic and polarimetric information of the scene on a monochromatic image plane. However, unlike the usual camera with an embedded demosaicing method, the latest polarized color camera is not delivered with an in-built demosaicing tool. For demosaicing, the users have to down-sample the captured images or to use traditional interpolation techniques. Neither of them can perform well since the polarization and color are interdependent. Therefore, joint chromatic and polarimetric demosaicing is the key to obtaining high-quality polarized color images. In this paper, we propose a joint chromatic and polarimetric demosaicing model to address this challenging problem. Instead of mechanically demosaicing for the multi-channel polarized color image, we further present a sparse representation-based optimization strategy that utilizes chromatic information and polarimetric information to jointly optimize the model. To avoid the interaction between color and polarization during demosaicing, we separately construct the corresponding dictionaries. We also build an optical data acquisition system to collect a dataset, which contains various sources of polarization, such as illumination, reflectance and birefringence. Results of both qualitative and quantitative experiments have shown that our method is capable of faithfully recovering full RGB information of four polarization angles for each pixel from a single mosaic input image. Moreover, the proposed method can perform well not only on the synthetic data but the real captured data.

Since specular reflection often exists in the real captured images and causes deviation between the recorded color and intrinsic color, specular reflection separation can bring advantages to multiple applications that require consistent object surface appearance. However, due to the color of an object is significantly influenced by the color of the illumination, the existing researches still suffer from the near-duplicate challenge, that is, the separation becomes unstable when the illumination color is close to the surface color. In this paper, we derive a polarization guided model to incorporate the polarization information into a designed iteration optimization separation strategy to separate the specular reflection. Based on the analysis of polarization, we propose a polarization guided model to generate a polarization chromaticity image, which is able to reveal the geometrical profile of the input image in complex scenarios, e.g., diversity of illumination. The polarization chromaticity image can accurately cluster the pixels with similar diffuse color. We further use the specular separation of all these clusters as an implicit prior to ensure that the diffuse component will not be mistakenly separated as the specular component. With the polarization guided model, we reformulate the specular reflection separation into a unified optimization function which can be solved by the ADMM strategy. The specular reflection will be detected and separated jointly by RGB and polarimetric information. Both qualitative and quantitative experimental results have shown that our method can faithfully separate the specular reflection, especially in some challenging scenarios.

We present a system for adaptive synthesis of indoor scenes given an empty room and only a few object categories. Automatically suggesting indoor objects and proper layouts to convert an empty room to a 3D scene is challenging, since it requires interior design knowledge to balance the factors like space, path distance, illumination and object relations, in order to insure the functional plausibility of the synthesized scenes. We exploit a database of 2D floor plans to extract object relations and provide layout examples for scene synthesis. With the labeled human positions and directions in each plan, we detect the activity relations and compute the coexistence frequency of object pairs to construct activity-associated object relation graphs. Given the input room and user-specified object categories, our system first leverages the object relation graphs and the database floor plans to suggest more potential object categories beyond the specified ones to make resulting scenes functionally complete, and then uses the similar plan references to create the layout of synthesized scenes. We show various synthesis results to demonstrate the practicability of our system, and validate its usability via a user study. We also compare our system with the state-of-the-art furniture layout and activity-centric scene representation methods, in terms of functional plausibility and user friendliness.