Scalable real-time volumetric surface reconstruction

By Jiawen Chen, Dennis Bautembach, Shahram Izadi. 2013

290 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, neutral): “The RGB-D scanning system must be trivial to use, the data processing must be robust and automatic, the semantic annotations must be crowdsourced, and the ﬂow of data through the system must be handled by a tracking server.”
Hierarchical Surface Prediction for 3D Object Reconstruction (2017, methodology): “It is inspired from octree formulations [5], [31] used in traditional multi-view reconstruction approaches.”
Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis (2016, background): “Since the introduction of commodity range sensors such as the Microsoft Kinect, RGB-D scanning has gained a huge momentum in both ofﬂine and real-time contexts [26, 3, 28, 42, 4, 7].”
Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields (2015, methodology): “Some works have either used octrees [24] [30] [31], or use a moving volume[12].”
Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM (2023, background): “Subsequent works focused on addressing the scalability issue by adopting more efficient data structures, such as surface elements (surfels) [35, 36], VoxelHashing [3, 9, 19] or Octrees [31,41].”
School of Electrical and Computer Engineering Oklahoma State University , Stillwater , OK 74078 , USA (methodology): “Kinfu Large Scale [15], voxel hashing [19], and scalable surface reconstruction [2] use depth sequences to generate 3D models.”
VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction (2016, methodology): “Recently, the scene representation has been extended to scale to larger reconstruction volumes [3, 4, 30, 8, 5, 6].”
Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction (2015, methodology): “This insight has acted as a catalyst for works such as the hash-based method of [22] and the octree technique of [21].”
Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices (2015, methodology): “One established approach for dealing with this problem [16, 2] is to swap data between the limited device memory and some much larger long term storage, which could be some host (CPU) memory, or the disk or memory card of a tablet.”
Deferred neural rendering (2019, background): “…with commodity RGB-D sensors, researchers have made significant progress, enabling robust tracking [Choi et al. 2015; Dai et al. 2017; Izadi et al. 2011; Newcombe et al. 2011; Whelan et al. 2016] and large-scale 3D scene capture [Chen et al. 2013; Nießner et al. 2013; Zeng et al. 2013].”
An Octree-Based Approach towards Efficient Variational Range Data Fusion (2016, methodology): “Recent work ([7, 22, 27]) uses Octrees for range data integration to map the environment, but employs simple update rules to encompass newly seen data without any optimization whatsoever.”
Real-time large-scale dense RGB-D SLAM with volumetric fusion (2014, methodology): “The optimisation consistently converges to a satisfactory result with these weights, similar to the findings of Chen et al. (2012). As highlighted in previous work, the Jacobian matrix in this problem is sparse, enabling the use of sparse linear algebra libraries for efficient optimisation. We use t”