SuBloNet: Sparse Super Block Networks for Large Scale Volumetric Fusion
| dc.contributor.author | Rückert, Darius | en_US |
| dc.contributor.author | Stamminger, Marc | en_US |
| dc.contributor.editor | Andres, Bjoern and Campen, Marcel and Sedlmair, Michael | en_US |
| dc.date.accessioned | 2021-09-25T16:36:28Z | |
| dc.date.available | 2021-09-25T16:36:28Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Training and inference of convolutional neural networks (CNNs) on truncated signed distance fields (TSDFs) is a challenging task. Large parts of the scene are usually empty, which makes dense implementations inefficient in terms of memory consumption and compute throughput. However, due to the truncation distance, non-zero values are grouped around the surface creating small dense blocks inside the large empty space. We show that this structure can be exploited by storing the TSDF in a block sparse tensor and then decomposing it into rectilinear super blocks. A super block is a dense 3d cuboid of variable size and can be processed by conventional CNNs. We analyze the rectilinear decomposition and present a formulation for computing the bandwidth-optimal solution given a specific network architecture. However, this solution is NP-complete, therefore we also a present a heuristic approach for fast training and inference tasks. We verify the effectiveness of SuBloNet and report a speedup of 4x towards dense implementations and 1.7x towards state-of-the-art sparse implementations. Using the super block architecture, we show that recurrent volumetric fusion is now possible on large scale scenes. Such a systems is able to reconstruct high-quality surfaces from few noisy depth images. | en_US |
| dc.description.sectionheaders | Capturing and Rendering | |
| dc.description.seriesinformation | Vision, Modeling, and Visualization | |
| dc.identifier.doi | 10.2312/vmv.20211375 | |
| dc.identifier.isbn | 978-3-03868-161-8 | |
| dc.identifier.pages | 91-98 | |
| dc.identifier.uri | https://doi.org/10.2312/vmv.20211375 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vmv20211375 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Reconstruction | |
| dc.subject | Mixed / augmented reality | |
| dc.subject | 3D imaging | |
| dc.title | SuBloNet: Sparse Super Block Networks for Large Scale Volumetric Fusion | en_US |
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