The latest advancements in scene relighting have been predominantly driven by inverse rendering with 3D Gaussian Splatting (3DGS). However, existing methods remain overly reliant on precise camera parameters under static illumination conditions, which is prohibitively expensive and even impractical in real-world scenarios. In this paper, we propose a novel learning from Unposed views under Varied illuminations Relightable 3D Gaussian Splatting (dubbed UV-RGS), to address this challenge by jointly optimizing camera poses, 3DGS representations, surface materials, and environment illuminations (i.e., unknown and varied lighting conditions in training) using only unposed views under varied lightings. Firstly, UV-RGS presents a viewpoint dividing strategy to group inputs into constituent units, enabling each unit can perform similar poses and illuminations. Next, for each unit, to get the constituent model, UV-RGS establishes an incrementally pose learning module to estimate coarse camera parameters, which also enjoy a proxy-view refinement to alleviate the sparse view learning. Additionally, for all constituent unit models, we introduce a holistic model learning strategy that integrates progressive unit aggregation component and the 3DGS coupled with camera poses joint optimization, which realizes the scene high-fidelity perception by the physical-based rendering. Extensive experiments on both real-world and synthetic challenging datasets demonstrate the effectiveness of UV-RGS, achieving the state-of-the-art performance for scene inverse rendering by learning 3DGS from only unposed views under varied illuminations.

Gaussian splatting techniques have recently revolutionized outdoor scene decomposition and relighting through multi-view images. However, achieving high rendering quality still requires a fixed lighting condition among all input views, which is costly or even impractical to capture in outdoor scenes. In this paper, we propose outdoor scene decomposition and relighting with 2D Gaussian splatting (OSDR-GS), a novel inverse rendering strategy under outdoor changing and unknown lighting conditions. Firstly, we present a lighting-based group learning framework that categorizes input images into multiple lighting groups, to learn the separate lighting from each group individually. Secondly, OSDR-GS introduces a fine-grained outdoor lighting component to represent sun-light and sky-light, respectively, which are also adjusted via the correlative exposure factors adaptively. Finally, we construct a visibility-driven shadow module to characterize the nuanced interplay of light and occlusion realistically, for eliminating the uncertainty of dark pixels on lighting-based group learning. Extensive experiments on multiple challenging outdoor datasets validate the effectiveness of OSDR-GS, which achieves the state-of-the-art performance in changing lighting scene inverse rendering.

Recent advances in 3D scene representation, particularly 3D Gaussian Splatting (3DGS), have demonstrated remarkable photorealistic rendering capabilities. However, the heavy reliance on dense and precisely calibrated camera configurations limits effectiveness in sparse view and unposed scenarios. In this paper, we present Tri-consistency 3D Gaussian Splatting (dubbed TriGS), a novel framework that jointly optimizes 3DGS parameters and camera poses only from sparse and unposed images via triple consistency supervisions coupled with the adaptive regularization strategy. We first estimate coarse camera poses by exploiting 3DGS's anisotropic properties through iterative relative pose optimization. Building upon this foundation, we introduce cross-view consistency enforcement through synchronized photometric color, geometric structure, and deep feature, effectively resolving rendering ambiguities with auxiliary supervisions. A unified rendering paradigm is also proposed to jointly refine Gaussian primitives and camera poses by transforming positions, covariances, and spherical harmonics. To combat overfitting inherent in joint optimization, we devise an adaptive regularization mechanism that strategically samples hard viewpoints based on baseline distances and training dynamics, enforcing projection consistency through deep feature priors. Extensive experiments on multiple challenging real-world datasets validate the effectiveness of TriGS, which achieves satisfactory results to set a new state-of-the-art without the reliance on external pose priors only under sparse and unposed view inputs.

The latest progress in novel view synthesis can be attributed to the Neural Radiance Field (NeRF), which requires densely sampled images with precise camera poses. However, collecting dense input images for a NeRF with accurate camera poses is highly expensive in many real-world scenarios. In this paper, we propose to learn Geometry Consistent Neural Radiance Field (GC-NeRF), to tackle this challenge by jointly optimizing a NeRF and its corresponding camera poses with sparse (as low as 2) and unposed views. First, the proposed GC-NeRF establishes image-level geometric consistencies, by producing photometric constraints from inter- and intra-views to update the NeRF and the camera poses in a fine-grained manner. Then, we adopt geometry projection with camera extrinsic parameters to further provide region-level consistency supervisions, which constructs pseudo-pixel labels to capture critical matching correlations. Moreover, we present an adaptive high-frequency mapping function to augment the geometry and texture information of the 3D scene. Extensive experiments on multiple challenging real-world datasets validate the effectiveness of the proposed GC-NeRF, which sets a new state-of-the-art for effectively learning NeRF with sparse and unposed views.