Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Adaptively sampled distance fields: a general representation of shape for computer graphics
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Automatic Camera Recovery for Closed or Open Image Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Interactive relighting of dynamic refractive objects
ACM SIGGRAPH 2008 papers
Real-time dense geometry from a handheld camera
Proceedings of the 32nd DAGM conference on Pattern recognition
Data-Parallel Octrees for Surface Reconstruction
IEEE Transactions on Visualization and Computer Graphics
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Proceedings of the 24th annual ACM symposium on User interface software and technology
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
DTAM: Dense tracking and mapping in real-time
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Octree-based fusion for realtime 3D reconstruction
Graphical Models
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KinectFusion is a real time 3D reconstruction system based on a low-cost moving depth camera and commodity graphics hardware. It represents the reconstructed surface as a signed distance function, and stores it in uniform volumetric grids. Though the uniform grid representation has advantages for parallel computation on GPU, it requires a huge amount of GPU memory. This paper presents a memory-efficient implementation of KinectFusion. The basic idea is to design an octree-based data structure on GPU, and store the signed distance function on data nodes. Based on the octree structure, we redesign reconstruction update and surface prediction to highly utilize parallelism of GPU. In the reconstruction update step, we first perform "add nodes" operations in a level-order manner, and then update the signed distance function. In the surface prediction step, we adopt a top-down ray tracing method to estimate the surface of the scene. In our experiments, our method costs less than 10% memory of KinectFusion while still being fast. Consequently, our method can reconstruct scenes 8 times larger than the original KinectFusion on the same hardware setup.