DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2411.17660v2
• Date: Fri, 29 Nov 2024 18:31:24 GMT
• Title: DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting
• Authors: Christian Homeyer, Leon Begiristain, Christoph Schnörr,
• Abstract summary: We introduce a SLAM system based on an end-to-end Tracker and extend it with a Renderer based on recent 3D Gaussian Splatting techniques. Our framework textbfDroidSplat achieves both SotA tracking and rendering results on common SLAM benchmarks.
• Score: 3.2771631221674333
• License:
• Abstract: Recent progress in scene synthesis makes standalone SLAM systems purely based on optimizing hyperprimitives with a Rendering objective possible. However, the tracking performance still lacks behind traditional and end-to-end SLAM systems. An optimal trade-off between robustness, speed and accuracy has not yet been reached, especially for monocular video. In this paper, we introduce a SLAM system based on an end-to-end Tracker and extend it with a Renderer based on recent 3D Gaussian Splatting techniques. Our framework \textbf{DroidSplat} achieves both SotA tracking and rendering results on common SLAM benchmarks. We implemented multiple building blocks of modern SLAM systems to run in parallel, allowing for fast inference on common consumer GPU's. Recent progress in monocular depth prediction and camera calibration allows our system to achieve strong results even on in-the-wild data without known camera intrinsics. Code will be available at \url{https://github.com/ChenHoy/DROID-Splat}.

Related papers

• GARAD-SLAM: 3D GAussian splatting for Real-time Anti Dynamic SLAM [9.060527946525381]
  We propose GARAD-SLAM, a real-time 3DGS-based SLAM system tailored for dynamic scenes. In terms of tracking, we directly perform dynamic segmentation on Gaussians and map them back to the front-end to obtain dynamic point labels. Our results on real-world datasets demonstrate that our method is competitive in tracking compared to baseline methods.
  arXiv  Detail & Related papers  (2025-02-05T14:44:17Z)
• Occam's LGS: A Simple Approach for Language Gaussian Splatting [57.00354758206751]
  We show that sophisticated techniques for language-grounded 3D Gaussian Splatting are simply unnecessary. We apply Occam's razor to the task at hand and perform weighted multi-view feature aggregation. Our results offer us state-of-the-art results with a speed-up of two orders of magnitude.
  arXiv  Detail & Related papers  (2024-12-02T18:50:37Z)
• FlashSLAM: Accelerated RGB-D SLAM for Real-Time 3D Scene Reconstruction with Gaussian Splatting [14.130327598928778]
  FlashSLAM is 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. Our method achieves up to a 92% improvement in average tracking accuracy over previous methods.
  arXiv  Detail & Related papers  (2024-12-01T05:44:38Z)
• HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction [38.47566815670662]
  HI-SLAM2 is a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. We demonstrate significant improvements over existing Neural SLAM methods and even surpass RGB-D-based methods in both reconstruction and rendering quality.
  arXiv  Detail & Related papers  (2024-11-27T01:39:21Z)
• IG-SLAM: Instant Gaussian SLAM [6.228980850646457]
  3D Gaussian Splatting has recently shown promising results as an alternative scene representation in SLAM systems. We present IG-SLAM, a dense RGB-only SLAM system that employs robust Dense-SLAM methods for tracking and combines them with Gaussian Splatting. We demonstrate competitive performance with state-of-the-art RGB-only SLAM systems while achieving faster operation speeds.
  arXiv  Detail & Related papers  (2024-08-02T09:07:31Z)
• Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians [87.48403838439391]
  3D Splatting has emerged as a powerful representation of geometry and appearance for RGB-only dense Simultaneous SLAM. We propose the first RGB-only SLAM system with a dense 3D Gaussian map representation. Our experiments on the Replica, TUM-RGBD, and ScanNet datasets indicate the effectiveness of globally optimized 3D Gaussians.
  arXiv  Detail & Related papers  (2024-05-26T12:26:54Z)
• MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements [59.70107451308687]
  We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior rendering by enabling faster scale awareness, and improved trajectory tracking. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit.
  arXiv  Detail & Related papers  (2024-04-01T04:57:41Z)
• Gaussian Splatting SLAM [16.3858380078553]
  We present the first application of 3D Gaussian Splatting in monocular SLAM. Our method runs live at 3fps, unifying the required representation for accurate tracking, mapping, and high-quality rendering. Several innovations are required to continuously reconstruct 3D scenes with high fidelity from a live camera.
  arXiv  Detail & Related papers  (2023-12-11T18:19:04Z)
• SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM [48.190398577764284]
  SplaTAM is an approach to enable high-fidelity reconstruction from a single unposed RGB-D camera. It employs a simple online tracking and mapping system tailored to the underlying Gaussian representation. Experiments show that SplaTAM achieves up to 2x superior performance in camera pose estimation, map construction, and novel-view synthesis over existing methods.
  arXiv  Detail & Related papers  (2023-12-04T18:53:24Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)
• Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios [70.70421502784598]
  RDS-SLAM can build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios.
  arXiv  Detail & Related papers  (2022-10-10T11:03:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.