Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noisy and inconsistent flow matching, making it difficult to handle challenging scenarios and long-sequence estimation.To overcome these challenges, we introduce Spatio-Temporal Visual Odometry (STVO), a novel deep network architecture that effectively leverages inherent spatio-temporal cues to enhance the accuracy and consistency of multi-frame flow matching. With more accurate and consistent flow matching, STVO can achieve better pose estimation through the bundle adjustment (BA).Specifically, STVO introduces two innovative components: 1) the Temporal Propagation Module that utilizes multi-frame information to extract and propagate temporal cues across adjacent frames, maintaining temporal consistency; 2) the Spatial Activation Module that utilizes geometric priors from the depth maps to enhance spatial consistency while filtering out excessive noise and incorrect matches.Our STVO achieves state-of-the-art performance on TUM-RGBD, EuRoc MAV, ETH3D and KITTI Odometry benchmarks. Notably, it improves accuracy by 77.8% on ETH3D benchmark and 38.9% on KITTI Odometry benchmark over the previous best methods.

Stereo matching recovers depth from image correspondences. Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth estimation and stereo matching. MonSter integrates monocular depth and stereo matching into a dual-branch architecture to iteratively improve each other. Confidence-based guidance adaptively selects reliable stereo cues for monodepth scale-shift recovery. The refined monodepth is in turn guides stereo effectively at ill-posed regions. Such iterative mutual enhancement enables MonSter to evolve monodepth priors from coarse object-level structures to pixel-level geometry, fully unlocking the potential of stereo matching. As shown in Fig. 2, MonSter ranks 1stacross five most commonly used leaderboards — SceneFlow, KITTI 2012, KITTI 2015, Middlebury, and ETH3D. Achieving up to 49.5% improvements (Bad 1.0 on ETH3D) over the previous best method. Comprehensive analysis verifies the effectiveness of MonSter in ill-posed regions. In terms of zero-shot generalization, MonSter significantly and consistently outperforms state-of-the-art across the board. The code is publicly available at: https://github.com/Junda24/MonSter.

Panoramic optical flow enables a comprehensive understanding of temporal dynamics across wide fields of view. However, severe distortions caused by sphere-to-plane projections, such as the equirectangular projection (ERP), significantly degrade the performance of conventional perspective-based optical flow methods, especially in polar regions. To address this challenge, we propose PriOr-Flow, a novel dual-branch framework that leverages the low-distortion nature of the orthogonal view to enhance optical flow estimation in these regions. Specifically, we introduce the Dual-Cost Collaborative Lookup (DCCL) operator, which jointly retrieves correlation information from both the primitive and orthogonal cost volumes, effectively mitigating distortion noise during cost volume construction. Furthermore, our Ortho-Driven Distortion Compensation (ODDC) module iteratively refines motion features from both branches, further suppressing polar distortions. Extensive experiments demonstrate that PriOr-Flow is compatible with various perspective-based iterative optical flow methods and consistently achieves state-of-the-art performance on publicly available panoramic optical flow datasets, setting a new benchmark for wide-field motion estimation. The code is publicly available at: https://github.com/longliangLiu/PriOr-Flow.

State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. Directly applying 2D convolutions for cost aggregation often results in edge blurring, detail loss, and mismatches in textureless regions. Some complex operations, like deformable convolutions and iterative warping, can partially alleviate this issue; however, they are not mobile-friendly, limiting their deployment on mobile devices. In this paper, we present a novel bilateral aggregation network (BANet) for mobile stereo matching that produces high-quality results with sharp edges and fine details using only 2D convolutions. Specifically, we first separate the full cost volume into detailed and smooth volumes using a spatial attention map, then perform detailed and smooth aggregations accordingly, ultimately fusing both to obtain the final disparity map. Experimental results demonstrate that our BANet-2D significantly outperforms other mobile-friendly methods, achieving 35.3\% higher accuracy on the KITTI 2015 leaderboard than MobileStereoNet-2D, with faster runtime on mobile devices. Code: \textcolor{magenta}{https://github.com/gangweix/BANet}.

The core of Multi-View Stereo (MVS) is to find corresponding pixels in neighboring images. However, due to challenging regions in input images such as untextured areas, repetitive patterns, or reflective surfaces, existing methods struggle to find precise pixel correspondence therein, resulting in inferior reconstruction quality. In this paper, we present an efficient context-perception MVS network, termed ACP-MVS. The ACP-MVS constructs a context-aware cost volume that can enhance pixels containing essential context information while suppressing irrelevant or noisy information via our proposed Context-stimulated Weighting Fusion module. Furthermore, we introduce a new Context-Guided Global Aggregation module, based on the insight that similar-looking pixels tend to have similar depths, which exploits global contextual cues to implicitly guide depth detail propagation from high-confidence regions to low-confidence ones. These two modules work in synergy to substantially improve reconstruction quality of ACP-MVS without incurring significant additional computational and time cost. Extensive experiments demonstrate that our approach not only achieves state-of-the-art performance but also offers the fastest inference speed and minimal GPU memory usage, providing practical value for practitioners working with high-resolution MVS image sets. Notably, our method ranks 2nd on the challenging Tanks and Temples advanced benchmark among all published methods. Code is available at https://github.com/HaoJia-mongh/ACP-MVS.

In this paper, a LiDAR-inertial odometry (LIO) method that eliminates the influence of moving objects in dynamic driving scenarios is proposed. This method constructs binarized labels for 3D points of current sweep, and utilizes the label difference between each point and its surrounding points in global map to identify moving objects. The surrounding points in global map are localized by voxel-location-based nearest neighbor search, without involving any massive computations. In addition, the proposed method is embeded into a LIO system (i.e., Dynamic-LIO), and achieves state-of-the-art performance on public datasets with extremlely low computational overhead (i.e., 1~9ms/sweep). We have released the source code of this work for the development of the community.

Stereo matching is a core component in many computer vision and robotics systems. Despite significant advances over the last decade, handling matching ambiguities in ill-posed regions and large disparities remains an open challenge. In this paper, we propose a new deep network architecture, called IGEV++, for stereo matching. The proposed IGEV++ constructs Multi-range Geometry Encoding Volumes (MGEV), which encode coarse-grained geometry information for ill-posed regions and large disparities, while preserving fine-grained geometry information for details and small disparities. To construct MGEV, we introduce an adaptive patch matching module that efficiently and effectively computes matching costs for large disparity ranges and/or ill-posed regions. We further propose a selective geometry feature fusion module to adaptively fuse multi-range and multi-granularity geometry features in MGEV. Then, we input the fused geometry features into ConvGRUs to iteratively update the disparity map. MGEV allows to efficiently handle large disparities and ill-posed regions, such as occlusions and textureless regions, and enjoys rapid convergence during iterations. Our IGEV++ achieves the best performance on the Scene Flow test set across all disparity ranges, up to 768px. Our IGEV++ also achieves state-of-the-art accuracy on the Middlebury, ETH3D, KITTI 2012, and 2015 benchmarks. Specifically, IGEV++ achieves a 3.23% 2-pixel outlier rate (Bad 2.0) on the large disparity benchmark, Middlebury, representing error reductions of 31.9% and 54.8% compared to RAFT-Stereo and GMStereo, respectively. We also present a real-time version of IGEV++ that achieves the best performance among all published real-time methods on the KITTI benchmarks.

Stereo matching is a fundamental task in scene comprehension. In recent years, the method based on iterative optimization has shown promise in stereo matching. However, the current iteration framework employs a single-peak lookup, which struggles to handle the multi-peak problem effectively. Additionally, the fixed search range used during the iteration process limits the final convergence effects. To address these issues, we present a novel iterative optimization architecture called MC-Stereo. This architecture mitigates the multi-peak distribution problem in matching through the multi-peak lookup strategy, and integrates the coarse-to-fine concept into the iterative framework via the cascade search range. Furthermore, given that feature representation learning is crucial for successful learn-based stereo matching, we introduce a pre-trained network to serve as the feature extractor, enhancing the front end of the stereo matching pipeline. Based on these improvements, MC-Stereo ranks first among all publicly available methods on the KITTI-2012 and KITTI-2015 benchmarks, and also achieves state-of-the-art performance on ETH3D. Code is available at https://github.com/MiaoJieF/MC-Stereo

In the current era of generative AI breakthroughs, generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterative or simultaneous multi-view inpainting. However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e.g., multiple beds in a bedroom) or requires time-consuming human text inputs for each view. We propose L-MAGIC, a novel method leveraging large language models for guidance while diffusing multiple coherent views of 360° panoramic scenes. L-MAGIC harnesses pretrained diffusion and language models without fine-tuning, ensuring zero-shot performance. The output quality is further enhanced by super-resolution and multi-view fusion techniques. Extensive experiments demonstrate that the resulting panoramic scenes feature better scene layouts and perspective view rendering quality compared to related works, with >70% preference in human evaluations. Combined with conditional diffusion models, L-MAGIC can accept various input modalities, including but not limited to text, depth maps, sketches, and colored scripts. Applying depth estimation further enables 3D point cloud generation and dynamic scene exploration with fluid camera motion. Code is available at https://github.com/ZhipengCai/L-MAGIC-code-release.

Multi- view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving. In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings. Surprisingly, we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings. To address this challenge, we propose a single-view and multi-view fused depth estimation system, which adaptively integrates high-confident multi-view and single-view results for both robust and accurate depth es-timations. The adaptive fusion module performs fusion by dynamically selecting high-confidence regions between two branches based on a wrapping confidence map. Thus, the system tends to choose the more reliable branch when facing textureless scenes, inaccurate calibration, dynamic ob-jects, and other degradation or challenging conditions. Our method outperforms state-of-the-art multi-view and fusion methods under robustness testing. Furthermore, we achieve state-of-the-art performance on challenging benchmarks (KITTI and DDAD) when given accurate pose estimations. Project website: https://github.com/Junda24/Afnet/.

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this article, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. The ACV can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy. We further design a fast version of ACV to enable real-time performance, named Fast-ACV, which generates high likelihood disparity hypotheses and the corresponding attention weights from low-resolution correlation clues to significantly reduce computational and memory cost and meanwhile maintain a satisfactory accuracy. Furthermore, we design a highly accurate network ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV respectively, which achieve state-of-the-art performance on several benchmarks.

Convolutional neural networks (CNNs) have shown attractive performance for stereo matching. However, spatially shared convolution weights of CNN-based methods usually face a dilemma that the convolution weights suitable for aggregating contextual information in smooth regions often blur local matching details of textured regions and vice versa. This paper tries to find a way out of the dilemma via a novel region separable stereo matching (RSSM) method, which is universally applicable to CNN stereo models based on 4D cost volumes and can greatly improve the accuracy and efficiency of existing models. The key idea of our method is to automatically group image pixels into regions according to the gradients, and then construct and process the respective cost volume of each region separately. To perform cost aggregation, we propose a two-stage network consisted of regional grouping aggregation (RGA) and regional fusion aggregation (RFA). In RGA, convolutions are grouped in channel-wise, and each group of convolutions learn dedicated weights for the corresponding region via regional supervision. Through RGA, each group of convolutions can extract the most representative features from the corresponding region. In RFA, we combine matching clues of all convolution groups from RGA to output the final prediction map. We further extend the idea of regional grouping to feature extraction and modify the skip connection in aggregation networks to better adapt our method to stereo matching models. Experimental results on five public datasets show that our method can significantly improve several state-of-the-art 3D CNN based stereo models.

LiDAR-assisted visual odometry (VO) is a widely-used solution for pose estimation and mapping. However, most existing LiDAR-assisted VO systems could suffer from the problems of 1) lacking distinctive and evenly distributed pixels for tracking due to the sparsity of LiDAR points and limited FOV overlap between a camera and LiDAR, and 2) nontrivial errors when processing LiDAR point clouds. To address above problems, we present CR-LDSO, a direct sparse LiDAR-assisted VO with the core parts being: 1) a novel cloud reusing method with point extraction/re-extraction to increase both the camera-LiDAR FOV overlap and the number of high-quality tracking pixels and 2) an occlusion removal method to exclude mismatching pixels due to occluded 3D object from sliding-window optimization and a point extraction strategy without depth interpolation. Extensive experimental results on public datasets demonstrates the superiority of our method to the existing state-of-the-art methods.

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities even for textureless regions. The proposed cost volume is named attention concatenation volume (ACV) which can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the aggregation network can achieve higher accuracy for GwcNet. Furthermore, we design a highly accurate network (ACVNet) based on our ACV, which achieves state-of-the-art performance on several benchmarks. The code is available at https://github.com/gangweiX/ACVNet.

In this paper, we present a novel deep learning and direct method based monocular visual odometry system named D2VO. Our system reconstructs the dense depth map of each keyframe and tracks camera poses based on these keyframes. Combining direct method and deep learning, both tracking and mapping of the system could benefit from the geometric measurement and semantic information. For each input frame, a feature pyramid is built and shared by both tracking and mapping process. The depth map of keyframe is efficiently estimated from coarse to fine with the followed multi-view hierarchical depth estimation network. We optimize the camera pose by minimizing photometric error between re-projected features of each frame and its reference keyframe with bundle adjustment. Experimental results on TUM dataset demonstrate that our approach outperforms the state-of-the-art methods on both tracking and mapping.