CoRBS: Comprehensive RGB-D benchmark for SLAM using Kinect v2

Wasenmüller, Oliver; Meyer, Marcel; Stricker, Didier

doi:10.1109/wacv.2016.7477636

Cited by 78 publications

(46 citation statements)

References 22 publications

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“…Camera poses and loop closures are computed via a state-of-the-art SLAM system [17]. We mainly evaluate our approach on the TUM RGB-D benchmark [44] and the ICL-NUIM dataset [45], but also use the pre-registered version of the CoRBS dataset [46]. Note that due to the nature of the topic, our approach is best seen in motion in the supplementary video (available at https://youtu.be/ouspbzHk5L0).…”

Section: Discussionmentioning

confidence: 99%

SurfelMeshing: Online Surfel-Based Mesh Reconstruction

Schöps

Sattler

Pollefeys

2020

IEEE Trans. Pattern Anal. Mach. Intell.

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We address the problem of mesh reconstruction from live RGB-D video, assuming a calibrated camera and poses provided externally (e.g., by a SLAM system). In contrast to most existing approaches, we do not fuse depth measurements in a volume but in a dense surfel cloud. We asynchronously (re)triangulate the smoothed surfels to reconstruct a surface mesh. This novel approach enables to maintain a dense surface representation of the scene during SLAM which can quickly adapt to loop closures. This is possible by deforming the surfel cloud and asynchronously remeshing the surface where necessary. The surfel-based representation also naturally supports strongly varying scan resolution. In particular, it reconstructs colors at the input camera's resolution. Moreover, in contrast to many volumetric approaches, ours can reconstruct thin objects since objects do not need to enclose a volume. We demonstrate our approach in a number of experiments, showing that it produces reconstructions that are competitive with the state-of-the-art, and we discuss its advantages and limitations. The algorithm (excluding loop closure functionality) is available as open source at https://github.com/puzzlepaint/surfelmeshing.

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Section: Discussionmentioning

confidence: 99%

SurfelMeshing: Online Surfel-Based Mesh Reconstruction

Schöps

Sattler

Pollefeys

2020

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Scenes : Our method can be applied to extract sharp features in an entire scene. We used two synthetic scene data sets in [CZK15], which are RGB‐D video streams capturing realistically rendered rooms, as well as benchmark data captured using Kinect v2 [WMS16]. We ran our method on a selected subset of frames in two videos and tried with different number of frames (see Figure ).…”

Section: Experiments Resultsmentioning

confidence: 99%

Extracting Sharp Features from RGB‐D Images

Cao

et al. 2016

Computer Graphics Forum

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Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features.

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“…We use sequences from the following datasets: TU Munich RGB-D [33], NYU Depth V2 [35], Indoor RGB-D [36], CoRBS [37], and ICL-NUIM [38]. These datasets contain…”

Section: B Datasetmentioning

confidence: 99%

Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging

Noraky

Sze

2020

IEEE Trans. Circuits Syst. Video Technol.

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Depth sensing is useful in a variety of applications that range from augmented reality to robotics. Time-of-flight (TOF) cameras are appealing because they obtain dense depth measurements with minimal latency. However, for many batterypowered devices, the illumination source of a TOF camera is power hungry and can limit the battery life of the device. To address this issue, we present an algorithm that lowers the power for depth sensing by reducing the usage of the TOF camera and estimating depth maps using concurrently collected images. Our technique also adaptively controls the TOF camera and enables it when an accurate depth map cannot be estimated. To ensure that the overall system power for depth sensing is reduced, we design our algorithm to run on a low power embedded platform, where it outputs 640 × 480 depth maps at 30 frames per second. We evaluate our approach on several RGB-D datasets, where it produces depth maps with an overall mean relative error of 0.96% and reduces the usage of the TOF camera by 85%. When used with commercial TOF cameras, we estimate that our algorithm can lower the total power for depth sensing by up to 73%.

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CoRBS: Comprehensive RGB-D benchmark for SLAM using Kinect v2

Cited by 78 publications

References 22 publications

SurfelMeshing: Online Surfel-Based Mesh Reconstruction

SurfelMeshing: Online Surfel-Based Mesh Reconstruction

Extracting Sharp Features from RGB‐D Images

Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging

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