Pose_Estimation
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Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis
2024-12-04 18:59:24Qitao Zhao, Shubham TulsianiAbstract
Inferring the 3D structure underlying a set of multi-view images typically requires solving two co-dependent tasks -- accurate 3D reconstruction requires precise camera poses, and predicting camera poses relies on (implicitly or explicitly) modeling the underlying 3D. The classical framework of analysis by synthesis casts this inference as a joint optimization seeking to explain the observed pixels, and recent instantiations learn expressive 3D representations (e.g., Neural Fields) with gradient-descent-based pose refinement of initial pose estimates. However, given a sparse set of observed views, the observations may not provide sufficient direct evidence to obtain complete and accurate 3D. Moreover, large errors in pose estimation may not be easily corrected and can further degrade the inferred 3D. To allow robust 3D reconstruction and pose estimation in this challenging setup, we propose SparseAGS, a method that adapts this analysis-by-synthesis approach by: a) including novel-view-synthesis-based generative priors in conjunction with photometric objectives to improve the quality of the inferred 3D, and b) explicitly reasoning about outliers and using a discrete search with a continuous optimization-based strategy to correct them. We validate our framework across real-world and synthetic datasets in combination with several off-the-shelf pose estimation systems as initialization. We find that it significantly improves the base systems' pose accuracy while yielding high-quality 3D reconstructions that outperform the results from current multi-view reconstruction baselines.
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URL
https://arxiv.org/abs/2412.03570
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NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images
2024-12-04 17:58:03Lingen Li, Zhaoyang Zhang, Yaowei Li, Jiale Xu, Xiaoyu Li, Wenbo Hu, Weihao Cheng, Jinwei Gu, Tianfan Xue, Ying ShanAbstract
Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multi-view alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when alignment is unstable due to insufficient overlap or occlusions between views. In this paper, we propose NVComposer, a novel approach that eliminates the need for explicit external alignment. NVComposer enables the generative model to implicitly infer spatial and geometric relationships between multiple conditional views by introducing two key components: 1) an image-pose dual-stream diffusion model that simultaneously generates target novel views and condition camera poses, and 2) a geometry-aware feature alignment module that distills geometric priors from dense stereo models during training. Extensive experiments demonstrate that NVComposer achieves state-of-the-art performance in generative multi-view NVS tasks, removing the reliance on external alignment and thus improving model accessibility. Our approach shows substantial improvements in synthesis quality as the number of unposed input views increases, highlighting its potential for more flexible and accessible generative NVS systems.
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URL
https://arxiv.org/abs/2412.03517
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A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks
2024-12-04 17:39:55Proma Hossain Progga, Md. Jobayer Rahman, Swapnil Biswas, Md. Shakil Ahmed, Arif Reza Anwary, Swakkhar ShatabdaAbstract
Gait recognition is a significant biometric technique for person identification, particularly in scenarios where other physiological biometrics are impractical or ineffective. In this paper, we address the challenges associated with gait recognition and present a novel approach to improve its accuracy and reliability. The proposed method leverages advanced techniques, including sequential gait landmarks obtained through the Mediapipe pose estimation model, Procrustes analysis for alignment, and a Siamese biGRU-dualStack Neural Network architecture for capturing temporal dependencies. Extensive experiments were conducted on large-scale cross-view datasets to demonstrate the effectiveness of the approach, achieving high recognition accuracy compared to other models. The model demonstrated accuracies of 95.7%, 94.44%, 87.71%, and 86.6% on CASIA-B, SZU RGB-D, OU-MVLP, and Gait3D datasets respectively. The results highlight the potential applications of the proposed method in various practical domains, indicating its significant contribution to the field of gait recognition.
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URL
https://arxiv.org/abs/2412.03498
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MCVO: A Generic Visual Odometry for Arbitrarily Arranged Multi-Cameras
2024-12-04 09:13:03Huai Yu, Junhao Wang, Yao He, Wen Yang, Gui-Song XiaAbstract
Making multi-camera visual SLAM systems easier to set up and more robust to the environment is always one of the focuses of vision robots. Existing monocular and binocular vision SLAM systems have narrow FoV and are fragile in textureless environments with degenerated accuracy and limited robustness. Thus multi-camera SLAM systems are gaining attention because they can provide redundancy for texture degeneration with wide FoV. However, current multi-camera SLAM systems face massive data processing pressure and elaborately designed camera configurations, leading to estimation failures for arbitrarily arranged multi-camera systems. To address these problems, we propose a generic visual odometry for arbitrarily arranged multi-cameras, which can achieve metric-scale state estimation with high flexibility in the cameras' arrangement. Specifically, we first design a learning-based feature extraction and tracking framework to shift the pressure of CPU processing of multiple video streams. Then we use the rigid constraints between cameras to estimate the metric scale poses for robust SLAM system initialization. Finally, we fuse the features of the multi-cameras in the SLAM back-end to achieve robust pose estimation and online scale optimization. Additionally, multi-camera features help improve the loop detection for pose graph optimization. Experiments on KITTI-360 and MultiCamData datasets validate the robustness of our method over arbitrarily placed cameras. Compared with other stereo and multi-camera visual SLAM systems, our method obtains higher pose estimation accuracy with better generalization ability. Our codes and online demos are available at \url{this https URL}
Abstract (translated)
URL
https://arxiv.org/abs/2412.03146
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An indoor DSO-based ceiling-vision odometry system for indoor industrial environments
2024-12-04 01:36:54Abdelhak Bougouffa, Emmanuel Seignez, Samir Bouaziz, Florian GardesAbstract
Autonomous Mobile Robots operating in indoor industrial environments require a localization system that is reliable and robust. While Visual Odometry (VO) can offer a reasonable estimation of the robot's state, traditional VO methods encounter challenges when confronted with dynamic objects in the scene. Alternatively, an upward-facing camera can be utilized to track the robot's movement relative to the ceiling, which represents a static and consistent space. We introduce in this paper Ceiling-DSO, a ceiling-vision system based on Direct Sparse Odometry (DSO). Unlike other ceiling-vision systems, Ceiling-DSO takes advantage of the versatile formulation of DSO, avoiding assumptions about observable shapes or landmarks on the ceiling. This approach ensures the method's applicability to various ceiling types. Since no publicly available dataset for ceiling-vision exists, we created a custom dataset in a real-world scenario and employed it to evaluate our approach. By adjusting DSO parameters, we identified the optimal fit for online pose estimation, resulting in acceptable error rates compared to ground truth. We provide in this paper a qualitative and quantitative analysis of the obtained results.
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URL
https://arxiv.org/abs/2412.02950
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EgoCast: Forecasting Egocentric Human Pose in the Wild
2024-12-03 23:11:06Maria Escobar, Juanita Puentes, Cristhian Forigua, Jordi Pont-Tuset, Kevis-Kokitsi Maninis, Pablo ArbelaezAbstract
Accurately estimating and forecasting human body pose is important for enhancing the user's sense of immersion in Augmented Reality. Addressing this need, our paper introduces EgoCast, a bimodal method for 3D human pose forecasting using egocentric videos and proprioceptive data. We study the task of human pose forecasting in a realistic setting, extending the boundaries of temporal forecasting in dynamic scenes and building on the current framework for current pose estimation in the wild. We introduce a current-frame estimation module that generates pseudo-groundtruth poses for inference, eliminating the need for past groundtruth poses typically required by current methods during forecasting. Our experimental results on the recent Ego-Exo4D and Aria Digital Twin datasets validate EgoCast for real-life motion estimation. On the Ego-Exo4D Body Pose 2024 Challenge, our method significantly outperforms the state-of-the-art approaches, laying the groundwork for future research in human pose estimation and forecasting in unscripted activities with egocentric inputs.
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URL
https://arxiv.org/abs/2412.02903
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ProbPose: A Probabilistic Approach to 2D Human Pose Estimation
Abstract
Current Human Pose Estimation methods have achieved significant improvements. However, state-of-the-art models ignore out-of-image keypoints and use uncalibrated heatmaps as keypoint location representations. To address these limitations, we propose ProbPose, which predicts for each keypoint: a calibrated probability of keypoint presence at each location in the activation window, the probability of being outside of it, and its predicted visibility. To address the lack of evaluation protocols for out-of-image keypoints, we introduce the CropCOCO dataset and the Extended OKS (Ex-OKS) metric, which extends OKS to out-of-image points. Tested on COCO, CropCOCO, and OCHuman, ProbPose shows significant gains in out-of-image keypoint localization while also improving in-image localization through data augmentation. Additionally, the model improves robustness along the edges of the bounding box and offers better flexibility in keypoint evaluation. The code and models are available on this https URL for research purposes.
Abstract (translated)
URL
https://arxiv.org/abs/2412.02254
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Cascaded Multi-Scale Attention for Enhanced Multi-Scale Feature Extraction and Interaction with Low-Resolution Images
2024-12-03 06:23:19Xiangyong Lu, Masanori Suganuma, Takayuki OkataniAbstract
In real-world applications of image recognition tasks, such as human pose estimation, cameras often capture objects, like human bodies, at low resolutions. This scenario poses a challenge in extracting and leveraging multi-scale features, which is often essential for precise inference. To address this challenge, we propose a new attention mechanism, named cascaded multi-scale attention (CMSA), tailored for use in CNN-ViT hybrid architectures, to handle low-resolution inputs effectively. The design of CMSA enables the extraction and seamless integration of features across various scales without necessitating the downsampling of the input image or feature maps. This is achieved through a novel combination of grouped multi-head self-attention mechanisms with window-based local attention and cascaded fusion of multi-scale features over different scales. This architecture allows for the effective handling of features across different scales, enhancing the model's ability to perform tasks such as human pose estimation, head pose estimation, and more with low-resolution images. Our experimental results show that the proposed method outperforms existing state-of-the-art methods in these areas with fewer parameters, showcasing its potential for broad application in real-world scenarios where capturing high-resolution images is not feasible. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2412.02197
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CLERF: Contrastive LEaRning for Full Range Head Pose Estimation
2024-12-03 01:08:03Ting-Ruen Wei, Haowei Liu, Huei-Chung Hu, Xuyang Wu, Yi Fang, Hsin-Tai WuAbstract
We introduce a novel framework for representation learning in head pose estimation (HPE). Previously such a scheme was difficult due to head pose data sparsity, making triplet sampling infeasible. Recent progress in 3D generative adversarial networks (3D-aware GAN) has opened the door for easily sampling triplets (anchor, positive, negative). We perform contrastive learning on extensively augmented data including geometric transformations and demonstrate that contrastive learning allows networks to learn genuine features that contribute to accurate HPE. On the other hand, we observe that existing HPE works struggle to predict head poses as accurately when test image rotation matrices are slightly out of the training dataset distribution. Experiments show that our methodology performs on par with state-of-the-art models on standard test datasets and outperforms them when images are slightly rotated/ flipped or full range head pose. To the best of our knowledge, we are the first to deliver a true full range HPE model capable of accurately predicting any head pose including upside-down pose. Furthermore, we compared with other existing full-yaw range models and demonstrated superior results.
Abstract (translated)
URL
https://arxiv.org/abs/2412.02066
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emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation
2024-12-02 23:39:37Sasha Salter, Richard Warren, Collin Schlager, Adrian Spurr, Shangchen Han, Rohin Bhasin, Yujun Cai, Peter Walkington, Anuoluwapo Bolarinwa, Robert Wang, Nathan Danielson, Josh Merel, Eftychios Pnevmatikakis, Jesse MarshallAbstract
Hands are the primary means through which humans interact with the world. Reliable and always-available hand pose inference could yield new and intuitive control schemes for human-computer interactions, particularly in virtual and augmented reality. Computer vision is effective but requires one or multiple cameras and can struggle with occlusions, limited field of view, and poor lighting. Wearable wrist-based surface electromyography (sEMG) presents a promising alternative as an always-available modality sensing muscle activities that drive hand motion. However, sEMG signals are strongly dependent on user anatomy and sensor placement, and existing sEMG models have required hundreds of users and device placements to effectively generalize. To facilitate progress on sEMG pose inference, we introduce the emg2pose benchmark, the largest publicly available dataset of high-quality hand pose labels and wrist sEMG recordings. emg2pose contains 2kHz, 16 channel sEMG and pose labels from a 26-camera motion capture rig for 193 users, 370 hours, and 29 stages with diverse gestures - a scale comparable to vision-based hand pose datasets. We provide competitive baselines and challenging tasks evaluating real-world generalization scenarios: held-out users, sensor placements, and stages. emg2pose provides the machine learning community a platform for exploring complex generalization problems, holding potential to significantly enhance the development of sEMG-based human-computer interactions.
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URL
https://arxiv.org/abs/2412.02725
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Detection, Pose Estimation and Segmentation for Multiple Bodies: Closing the Virtuous Circle
2024-12-02 14:50:15Miroslav Purkrabek, Jiri MatasAbstract
Human pose estimation methods work well on separated people but struggle with multi-body scenarios. Recent work has addressed this problem by conditioning pose estimation with detected bounding boxes or bottom-up-estimated poses. Unfortunately, all of these approaches overlooked segmentation masks and their connection to estimated keypoints. We condition pose estimation model by segmentation masks instead of bounding boxes to improve instance separation. This improves top-down pose estimation in multi-body scenarios but does not fix detection errors. Consequently, we develop BBox-Mask-Pose (BMP), integrating detection, segmentation and pose estimation into self-improving feedback loop. We adapt detector and pose estimation model for conditioning by instance masks and use Segment Anything as pose-to-mask model to close the circle. With only small models, BMP is superior to top-down methods on OCHuman dataset and to detector-free methods on COCO dataset, combining the best from both approaches and matching state of art performance in both settings. Code is available on this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2412.01562
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6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting
2024-12-02 14:32:19Yufeng Jin, Vignesh Prasad, Snehal Jauhri, Mathias Franzius, Georgia ChalvatzakiAbstract
Efficient and accurate object pose estimation is an essential component for modern vision systems in many applications such as Augmented Reality, autonomous driving, and robotics. While research in model-based 6D object pose estimation has delivered promising results, model-free methods are hindered by the high computational load in rendering and inferring consistent poses of arbitrary objects in a live RGB-D video stream. To address this issue, we present 6DOPE-GS, a novel method for online 6D object pose estimation \& tracking with a single RGB-D camera by effectively leveraging advances in Gaussian Splatting. Thanks to the fast differentiable rendering capabilities of Gaussian Splatting, 6DOPE-GS can simultaneously optimize for 6D object poses and 3D object reconstruction. To achieve the necessary efficiency and accuracy for live tracking, our method uses incremental 2D Gaussian Splatting with an intelligent dynamic keyframe selection procedure to achieve high spatial object coverage and prevent erroneous pose updates. We also propose an opacity statistic-based pruning mechanism for adaptive Gaussian density control, to ensure training stability and efficiency. We evaluate our method on the HO3D and YCBInEOAT datasets and show that 6DOPE-GS matches the performance of state-of-the-art baselines for model-free simultaneous 6D pose tracking and reconstruction while providing a 5$\times$ speedup. We also demonstrate the method's suitability for live, dynamic object tracking and reconstruction in a real-world setting.
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URL
https://arxiv.org/abs/2412.01543
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HandOS: 3D Hand Reconstruction in One Stage
2024-12-02 14:28:29Xingyu Chen, Zhuheng Song, Xiaoke Jiang, Yaoqing Hu, Junzhi Yu, Lei ZhangAbstract
Existing approaches of hand reconstruction predominantly adhere to a multi-stage framework, encompassing detection, left-right classification, and pose estimation. This paradigm induces redundant computation and cumulative errors. In this work, we propose HandOS, an end-to-end framework for 3D hand reconstruction. Our central motivation lies in leveraging a frozen detector as the foundation while incorporating auxiliary modules for 2D and 3D keypoint estimation. In this manner, we integrate the pose estimation capacity into the detection framework, while at the same time obviating the necessity of using the left-right category as a prerequisite. Specifically, we propose an interactive 2D-3D decoder, where 2D joint semantics is derived from detection cues while 3D representation is lifted from those of 2D joints. Furthermore, hierarchical attention is designed to enable the concurrent modeling of 2D joints, 3D vertices, and camera translation. Consequently, we achieve an end-to-end integration of hand detection, 2D pose estimation, and 3D mesh reconstruction within a one-stage framework, so that the above multi-stage drawbacks are overcome. Meanwhile, the HandOS reaches state-of-the-art performances on public benchmarks, e.g., 5.0 PA-MPJPE on FreiHand and 64.6\% PCK@0.05 on HInt-Ego4D. Project page: this http URL.
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URL
https://arxiv.org/abs/2412.01537
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SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames
2024-12-02 13:51:58Yuxuan Zhou, Xingxing Li, Shengyu Li, Chunxi Xia, Xuanbin Wang, Shaoquan FengAbstract
For high-level geo-spatial applications and intelligent robotics, accurate global pose information is of crucial importance. Map-aided localization is an important and universal approach to overcome the limitations of global navigation satellite system (GNSS) in challenging environments. However, current solutions face challenges in terms of mapping flexibility, storage burden and re-localization performance. In this work, we present SF-Loc, a lightweight visual mapping and map-aided localization system, whose core idea is the map representation based on sparse frames with dense (though downsampled) depth, termed as visual structure frames. In the mapping phase, multi-sensor dense bundle adjustment (MS-DBA) is applied to construct geo-referenced visual structure frames. The local co-visbility is checked to keep the map sparsity and achieve incremental mapping. In the localization phase, coarse-to-fine vision-based localization is performed, in which multi-frame information and the map distribution are fully integrated. To be specific, the concept of spatially smoothed similarity (SSS) is proposed to overcome the place ambiguity, and pairwise frame matching is applied for efficient and robust pose estimation. Experimental results on both public and self-made datasets verify the effectiveness of the system. In complex urban road scenarios, the map size is down to 3 MB per kilometer and stable decimeter-level re-localization can be achieved. The code will be made open-source soon (this https URL).
Abstract (translated)
URL
https://arxiv.org/abs/2412.01500
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MamKPD: A Simple Mamba Baseline for Real-Time 2D Keypoint Detection
2024-12-02 12:03:32Yonghao Dang, Liyuan Liu, Hui Kang, Ping Ye, Jianqin YinAbstract
Real-time 2D keypoint detection plays an essential role in computer vision. Although CNN-based and Transformer-based methods have achieved breakthrough progress, they often fail to deliver superior performance and real-time speed. This paper introduces MamKPD, the first efficient yet effective mamba-based pose estimation framework for 2D keypoint detection. The conventional Mamba module exhibits limited information interaction between patches. To address this, we propose a lightweight contextual modeling module (CMM) that uses depth-wise convolutions to model inter-patch dependencies and linear layers to distill the pose cues within each patch. Subsequently, by combining Mamba for global modeling across all patches, MamKPD effectively extracts instances' pose information. We conduct extensive experiments on human and animal pose estimation datasets to validate the effectiveness of MamKPD. Our MamKPD-L achieves 77.3% AP on the COCO dataset with 1492 FPS on an NVIDIA GTX 4090 GPU. Moreover, MamKPD achieves state-of-the-art results on the MPII dataset and competitive results on the AP-10K dataset while saving 85% of the parameters compared to ViTPose. Our project page is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2412.01422
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Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures
2024-12-02 09:13:14Qiyuan Shen, Hengwang Zhao, Weihao Yan, Chunxiang Wang, Tong Qin, Ming YangAbstract
Cross-modal localization has drawn increasing attention in recent years, while the visual relocalization in prior LiDAR maps is less studied. Related methods usually suffer from inconsistency between the 2D texture and 3D geometry, neglecting the intensity features in the LiDAR point cloud. In this paper, we propose a cross-modal visual relocalization system in prior LiDAR maps utilizing intensity textures, which consists of three main modules: map projection, coarse retrieval, and fine relocalization. In the map projection module, we construct the database of intensity channel map images leveraging the dense characteristic of panoramic projection. The coarse retrieval module retrieves the top-K most similar map images to the query image from the database, and retains the top-K' results by covisibility clustering. The fine relocalization module applies a two-stage 2D-3D association and a covisibility inlier selection method to obtain robust correspondences for 6DoF pose estimation. The experimental results on our self-collected datasets demonstrate the effectiveness in both place recognition and pose estimation tasks.
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URL
https://arxiv.org/abs/2412.01299
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CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
2024-12-02 02:26:21Jingnan Shi, Rajat Talak, Harry Zhang, David Jin, Luca CarloneAbstract
We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation pipeline. The pipeline implements an encoder-decoder model for shape estimation. It uses FiLM-conditioning for implicit shape reconstruction and a DPT-based network for estimating pose-normalized points for pose estimation. As a second contribution, we propose an optimization-based pose and shape corrector that can correct estimation errors caused by a domain gap. Observing that the shape decoder is well behaved in the convex hull of known shapes, we approximate the shape decoder with an active shape model, and show that this reduces the shape correction problem to a constrained linear least squares problem, which can be solved efficiently by an interior point algorithm. Third, we introduce a self-training pipeline to perform self-supervised domain adaptation of CRISP. The self-training is based on a correct-and-certify approach, which leverages the corrector to generate pseudo-labels at test time, and uses them to self-train CRISP. We demonstrate CRISP (and the self-training) on YCBV, SPE3R, and NOCS datasets. CRISP shows high performance on all the datasets. Moreover, our self-training is capable of bridging a large domain gap. Finally, CRISP also shows an ability to generalize to unseen objects. Code and pre-trained models will be available on this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2412.01052
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ESCAPE: Equivariant Shape Completion via Anchor Point Encoding
2024-12-01 20:05:14Burak Bekci, Nassir Navab, Federico Tombari, Mahdi SalehAbstract
Shape completion, a crucial task in 3D computer vision, involves predicting and filling the missing regions of scanned or partially observed objects. Current methods expect known pose or canonical coordinates and do not perform well under varying rotations, limiting their real-world applicability. We introduce ESCAPE (Equivariant Shape Completion via Anchor Point Encoding), a novel framework designed to achieve rotation-equivariant shape completion. Our approach employs a distinctive encoding strategy by selecting anchor points from a shape and representing all points as a distance to all anchor points. This enables the model to capture a consistent, rotation-equivariant understanding of the object's geometry. ESCAPE leverages a transformer architecture to encode and decode the distance transformations, ensuring that generated shape completions remain accurate and equivariant under rotational transformations. Subsequently, we perform optimization to calculate the predicted shapes from the encodings. Experimental evaluations demonstrate that ESCAPE achieves robust, high-quality reconstructions across arbitrary rotations and translations, showcasing its effectiveness in real-world applications without additional pose estimation modules.
Abstract (translated)
URL
https://arxiv.org/abs/2412.00952
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Particle-based 6D Object Pose Estimation from Point Clouds using Diffusion Models
2024-12-01 14:52:44Christian M\"oller, Niklas Funk, Jan PetersAbstract
Object pose estimation from a single view remains a challenging problem. In particular, partial observability, occlusions, and object symmetries eventually result in pose ambiguity. To account for this multimodality, this work proposes training a diffusion-based generative model for 6D object pose estimation. During inference, the trained generative model allows for sampling multiple particles, i.e., pose hypotheses. To distill this information into a single pose estimate, we propose two novel and effective pose selection strategies that do not require any additional training or computationally intensive operations. Moreover, while many existing methods for pose estimation primarily focus on the image domain and only incorporate depth information for final pose refinement, our model solely operates on point cloud data. The model thereby leverages recent advancements in point cloud processing and operates upon an SE(3)-equivariant latent space that forms the basis for the particle selection strategies and allows for improved inference times. Our thorough experimental results demonstrate the competitive performance of our approach on the Linemod dataset and showcase the effectiveness of our design choices. Code is available at this https URL .
Abstract (translated)
URL
https://arxiv.org/abs/2412.00835
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FlashSLAM: Accelerated RGB-D SLAM for Real-Time 3D Scene Reconstruction with Gaussian Splatting
2024-12-01 05:44:38Phu Pham, Damon Conover, Aniket BeraAbstract
We present FlashSLAM, a novel SLAM approach that leverages 3D Gaussian Splatting for efficient and robust 3D scene reconstruction. Existing 3DGS-based SLAM methods often fall short in sparse view settings and during large camera movements due to their reliance on gradient descent-based optimization, which is both slow and inaccurate. FlashSLAM addresses these limitations by combining 3DGS with a fast vision-based camera tracking technique, utilizing a pretrained feature matching model and point cloud registration for precise pose estimation in under 80 ms - a 90% reduction in tracking time compared to SplaTAM - without costly iterative rendering. In sparse settings, our method achieves up to a 92% improvement in average tracking accuracy over previous methods. Additionally, it accounts for noise in depth sensors, enhancing robustness when using unspecialized devices such as smartphones. Extensive experiments show that FlashSLAM performs reliably across both sparse and dense settings, in synthetic and real-world environments. Evaluations on benchmark datasets highlight its superior accuracy and efficiency, establishing FlashSLAM as a versatile and high-performance solution for SLAM, advancing the state-of-the-art in 3D reconstruction across diverse applications.
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URL
https://arxiv.org/abs/2412.00682
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