Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields

By Matthew Klingensmith, Ivan Dryanovski, Siddhartha S. Srinivasa, Jizhong Xiao. 2015

178 citations recorded by the source corpus.

We describe CHISEL: a system for real-time housescale (300 square meter or more) dense 3D reconstruction onboard a Google Tango [1] mobile device by using a dynamic spatially-hashed truncated signed distance field[2] for mapping, and visual-inertial odometry for localization. By aggressively culling parts of the scene that do not contain surfaces, we avoid needless computation and wasted memory. Even under very noisy conditions, we produce high-quality reconstructions through the use of space carving. We are able to reconstruct and render very large scenes at a resolution of 2-3 cm in real time on a mobile device without the use of GPU computing. The user is able to view and interact with the reconstruction in real-time through an intuitive interface. We provide both qualitative and quantitative results on publicly available RGB-D datasets [3], and on datasets collected in real-time from two devices.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “There is a large variety of algorithms targeting this scenario [59, 88, 7, 62, 37, 89, 42, 9, 90, 38, 12].”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “In the colored box below we provide an example of SLAM for a robot equipped with a monocular camera and a 3D laser scanner.”
Real-Time Large-Scale Dense 3D Reconstruction with Loop Closure (2016, background): “Drift can be reduced by additional sensing such as IMU measurements [28, 29].”
An Octree-Based Approach towards Efficient Variational Range Data Fusion (2016, background): “In [19] and later [12], the authors present an alternative to hierarchical partitioning by hashing the TSDF geometry to enable near-constant time lookups.”
Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning (2016, methodology): “There has also been work on speeding up these algorithms to run on CPU [11] and even on mobile devices [14]; however, the application of high-resolution 3D reconstruction remains the same.”