Recently, 3D Gaussian Splatting (3DGS) has shown encouraging performance for open vocabulary scene understanding tasks. However, previous methods cannot distinguish 3D instance-level information, which usually predicts a heatmap between the scene feature and text query. In this paper, we propose PanoGS, a novel and effective 3D panoptic open vocabulary scene understanding approach. Technically, to learn accurate 3D language features that can scale to large indoor scenarios, we adopt the pyramid tri-plane to model the latent continuous parametric feature space and use a 3D feature decoder to regress the multi-view fused 2D feature cloud. Besides, we propose language-guided graph cuts that synergistically leverage reconstructed geometry and learned language cues to group 3D Gaussian primitives into a set of super-primitives. To obtain 3D consistent instance, we perform graph clustering based segmentation with SAM-guided edge affinity computation between different super-primitives. Extensive experiments on widely used datasets show better or more competitive performance on 3D panoptic open vocabulary scene understanding. Project page: https://zju3dv.github.io/panogs.

Multi-session visual SLAM systems enable 6-DoF camera localization along with long-term maintenance and expansion of the global map, by utilizing image data from different sessions. However, in large-scale environments, these systems often suffer from severe scale drift. While modern SLAM systems attempt to maintain global map consistency through loop detection and correction, they still face challenges in terms of convergence and accuracy. In this paper, we propose a robust large-scale multi-session SLAM system for long-term localization and mapping that achieves global consistency. Furthermore, to address the backend optimization problem in large-scale environments, we introduce a hierarchical optimization strategy based on the graph structure. More specifically, a subgraph structure is introduced to reduce the size of problem while effectively propagating scale correction information. In addition, a hierarchical strategy enables coarse-to-fine updates of the graph states. Experimental results not only demonstrate that our method efficiently optimizes the pose graph and maintains map consistency in large-scale environments, but also highlight the effectiveness and scalability of the proposed approach.

Recently, neural radiance fields (NeRF) have gained significant attention in the field of visual localization. However, existing NeRF-based approaches either lack geometric constraints or require extensive storage for feature matching, limiting their practical applications. To address these challenges, we propose an efficient and novel visual localization approach based on the neural implicit map with complementary features. Specifically, to enforce geometric constraints and reduce storage requirements, we implicitly learn a 3D keypoint descriptor field, avoiding the need to explicitly store point-wise features. To further address the semantic ambiguity of descriptors, we introduce additional semantic contextual feature fields, which enhance the quality and reliability of 2D-3D correspondences. Besides, we propose descriptor similarity distribution alignment to minimize the domain gap between 2D and 3D feature spaces during matching. Finally, we construct the matching graph using both complementary descriptors and contextual features to establish accurate 2D3D correspondences for 6-DoF pose estimation. Compared with the recent NeRF-based approaches, our method achieves a$3 \times$faster training speed and a$45 \times$reduction in model storage. Extensive experiments on two widely used datasets demonstrate that our approach outperforms or is highly competitive with other state-of-the-art NeRF-based visual localization methods. Project page: https://zju3dv.github.io/neuraloc

Recent feature matching approaches like ETO have focused on developing lightweight matching algorithms for real-time applications. However, their lack of cross-image feature interaction and sufficient refinement often lead to a decline in matching accuracy. To address these challenges, we propose ETO+, a novel and accurate feature matching algorithm that incorporates a lightweight yet efficient bidirectional interaction module and multi-stage refinement. Specifically, we introduce Trans-CNN, a bidirectional feature interaction module that integrates CNN- and transformer-based techniques to enhance both intra-image feature refinement and inter-image feature fusion, all while maintaining a comparable computational cost. Furthermore, by leveraging the inherent sparsity of local feature matching, we propose an efficient strategy to adaptively reallocate computational resources within the network. Additionally, we design an adaptive loss function that mitigates the impact of large matching errors, thereby improving overall robustness. Extensive experiments on widely used datasets demonstrate that our approach achieves a strong balance between accuracy and computational efficiency. It outperforms ETO by 7.9 in AUC@5 on MegaDepth, respectively, while being about 40% faster than E-LoFTR.

3D reconstruction of indoor and urban environments is a prominent research topic with various downstream applications. However, existing geometric priors for addressing low-texture regions in indoor and urban settings often lack global consistency. Moreover, Gaussian Splatting and implicit SDF fields often suffer from discontinuities or exhibit computational inefficiencies, resulting in a loss of detail. To address these issues, we propose an Atlanta-world guided implicit-structured Gaussian Splatting that achieves smooth indoor and urban scene reconstruction while preserving high-frequency details and rendering efficiency. By leveraging the Atlanta-world model, we ensure the accurate surface reconstruction for low-texture regions, while the proposed novel implicit-structured GS representations provide smoothness without sacrificing efficiency and high-frequency details. Specifically, we propose a semantic GS representation to predict the probability of all semantic regions and deploy a structure plane regularization with learnable plane indicators for global accurate surface reconstruction. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in both indoor and urban scenes, delivering superior surface reconstruction quality.

Real-time 3D reconstruction is a fundamental task in computer graphics. Recently, differentiable-rendering-based SLAM system has demonstrated significant potential, enabling photorealistic scene rendering through learnable scene representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Current differentiable rendering methods face dual challenges in real-time computation and sensor noise sensitivity, leading to degraded geometric fidelity in scene reconstruction and limited practicality. To address these challenges, we propose a novel real-time system EGG-Fusion, featuring robust sparse-to-dense camera tracking and a geometry-aware Gaussian surfel mapping module, introducing an information filter-based fusion method that explicitly accounts for sensor noise to achieve high-precision surface reconstruction. The proposed differentiable Gaussian surfel mapping effectively models multi-view consistent surfaces while enabling efficient parameter optimization. Extensive experimental results demonstrate that the proposed system achieves a surface reconstruction error of 0.6cm on standardized benchmark datasets including Replica and ScanNet++, representing over 20% improvement in accuracy compared to state-of-the-art (SOTA) GS-based methods. Notably, the system maintains real-time processing capabilities at 24 FPS, establishing it as one of the most accurate differentiable-rendering-based real-time reconstruction systems. Project Page: https://zju3dv.github.io/eggfusion/.

Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches. Code and data are available at project page: https://zju3dv.github.io/splatloc.

Virtual content creation and interaction play an important role in modern 3D applications. Recovering detailed 3D models from real scenes can significantly expand the scope of its applications and has been studied for decades in the computer vision and computer graphics community. In this work, we propose Vox-Surf, a voxel-based implicit surface representation. Our Vox-Surf divides the space into finite sparse voxels, where each voxel is a basic geometry unit that stores geometry and appearance information on its corner vertices. Due to the sparsity inherited from the voxel representation, Vox-Surf is suitable for almost any scene and can be easily trained end-to-end from multiple view images. We utilize a progressive training process to gradually cull out empty voxels and keep only valid voxels for further optimization, which greatly reduces the number of sample points and improves inference speed. Experiments show that our Vox-Surf representation can learn fine surface details and accurate colors with less memory and faster rendering than previous methods. The resulting fine voxels can also be considered as the bounding volumes for collision detection, which is useful in 3D interactions. We also show the potential application of Vox-Surf in scene editing and augmented reality. The source code is publicly available at https://github.com/zju3dv/Vox-Surf.

In recent years, the paradigm of neural implicit representations has gained substantial attention in the field of Simultaneous Localization and Mapping (SLAM). However, a notable gap exists in the existing approaches when it comes to scene understanding. In this paper, we introduce NIS-SLAM, an efficient neural implicit semantic RGB-D SLAM system, that leverages a pre-trained 2D segmentation network to learn consistent semantic representations. Specifically, for high-fidelity surface reconstruction and spatial consistent scene understanding, we combine high-frequency multi-resolution tetrahedron-based features and low-frequency positional encoding as the implicit scene representations. Besides, to address the inconsistency of 2D segmentation results from multiple views, we propose a fusion strategy that integrates the semantic probabilities from previous non-keyframes into keyframes to achieve consistent semantic learning. Furthermore, we implement a confidence-based pixel sampling and progressive optimization weight function for robust camera tracking. Extensive experimental results on various datasets show the better or more competitive performance of our system when compared to other existing neural dense implicit RGB-D SLAM approaches. Finally, we also show that our approach can be used in augmented reality applications. Project page: https://zju3dv.github.io/nis_slam.

Deep hashing-based approaches have become the optimal solutions for large-scale image retrieval task due to their high computational efficiency and low storage burden. Some methods leverage a large teacher network to improve the retrieval performance of the small student network through knowledge distillation, which incurs high computational and time costs. In this paper, we propose Self-Distillation Hashing (SeDH), which improves the image retrieval performance without introducing a complex teacher model and significantly reduces the overall computation costs. Specifically, we generate the soft targets via ensembling the logits of other similar images among the mini-batch. The ensembled soft targets can model the relations between different image samples, which can act as additional supervision for classification. Besides, to learn more compact features and accurate inter-sample similarities, we propose a similarity-preserving loss on the learned hashing features, which aligns the softened similarity distribution with the pairwise soft similarity. Extensive experiments demonstrate that our approach can yield state-of-the-art performance on deep supervised hashing retrieval.

The combination of augmented reality (AR) and medicine is an important trend in current research. The powerful display and interaction capabilities of the AR system can assist doctors to perform more complex operations. Since the tooth itself is an exposed rigid body structure, dental AR is a relatively hot research direction with application potential. However, none of the existing dental AR solutions are designed for wearable AR devices such as AR glasses. At the same time, these methods rely on high-precision scanning equipment or auxiliary positioning markers, which greatly increases the operational complexity and cost of clinical AR. In this work, we propose a simple and accurate neural-implicit model-driven dental AR system, named ImTooth, and adapted for AR glasses. Based on the modeling capabilities and differentiable optimization properties of state-of-the-art neural implicit representations, our system fuses reconstruction and registration in a single network, greatly simplifying the existing dental AR solutions and enabling reconstruction, registration, and interaction. Specifically, our method learns a scale-preserving voxel-based neural implicit model from multi-view images captured from a textureless plaster model of the tooth. Apart from color and surface, we also learn the consistent edge feature inside our representation. By leveraging the depth and edge information, our system can register the model to real images without additional training. In practice, our system uses a single Microsoft HoloLens 2 as the only sensor and display device. Experiments show that our method can reconstruct high-precision models and accomplish accurate registration. It is also robust to weak, repeating and inconsistent textures. We also show that our system can be easily integrated into dental diagnostic and therapeutic procedures, such as bracket placement guidance.

In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods. Our approach is inspired by the recently developed implicit mapping and positioning system and further extends the idea so that it can be freely applied to practical scenarios. Specifically, we leverage a voxel-based neural implicit surface representation to encode and optimize the scene inside each voxel. Furthermore, we adopt an octree-based structure to divide the scene and support dynamic expansion, enabling our system to track and map arbitrary scenes without knowing the environment like in previous works. Moreover, we proposed a high-performance multi-process framework to speed up the method, thus supporting some applications that require real-time performance. The evaluation results show that our methods can achieve better accuracy and completeness than previous methods. We also show that our Vox-Fusion can be used in augmented reality and virtual reality applications. Our source code is publicly available at https://github.com/zju3dv/Vox-Fusion.

Robust and accurate global 6DoF localization is essential for many applications, i.e., augmented reality and autonomous driving. Most existing 6DoF visual localization approaches need to build a dense texture model in advance, which is computationally extensive and almost infeasible in the global range. In this work, we propose BDLoc, a hierarchical global localization framework via the 2.5D building map, which is able to estimate the accurate pose of the query street-view image without using detailed dense 3D model and texture information. Specifically speaking, we first extract the 3D building information from the street-view image and surrounding 2.5D building map, and then solve a coarse relative pose by local to global registration. In order to improve the feature extraction, we propose a novel SPG-Net which is able to capture both local and global features. Finally, an iterative semantic alignment is applied to obtain a finner result with the differentiable rendering and the cross-view semantic constraint. Except for a coarse longitude and latitude from GPS, BDLoc doesn’t need any additional information like altitude and orientation that are necessary for many previous works. We also create a large dataset to explore the performance of the 2.5D map-based localization task. Extensive experiments demonstrate the superior performance of our method.

Deep supervised hashing has emerged as an influential solution to large-scale semantic image retrieval problems in computer vision. In the light of recent progress, image label is the common way to define whether two images belong to the same category, but it contains little supervised information. The one-hot label can't accurately define the similarity of two images, which is important for image retrieval. In this paper, we propose an effective method, Deep Transfer Hashing(DTH) which uses the knowledge from teacher model as the supervised information. Inspired by knowledge distillation for model compression and deep hashing for fast image retrieval, we transfer the knowledge from a complex convolutional neural network(teacher) to a small neural network(student) which is used for fast image retrieval. The distance of the knowledge from teacher model can indicate the similarity of images. By minimizing the hashing codes distribution between the hashing layers of teacher model and student model, we can improve the retrieval performance. And we also evaluate the performance of the compressed model at inference stage. We test our method on widely used datasets CIFAR-10 and NUS-WIDE and we compare our method with other state-of-the-art methods in image retrieval domain. The experimental results show that our method can improve the image retrieval baseline by a large margin and better than other methods.