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- W4225331314 abstract "Recent years have witnessed a growing interest in online RGB-D 3D reconstruction. On the premise of ensuring the reconstruction accuracy with noisy depth scans, making the system scalable to various environments is still challenging. In this paper, we devote our efforts to try to fill in this research gap by proposing a scalable and robust RGB-D 3D reconstruction framework, namely Chunk-Fusion. In ChunkFusion, sparse voxel management is exploited to improve the scalability of online reconstruction. Besides, a chunk-wise TSDF (truncated signed distance function) fusion network is designed to perform a robust integration of the noisy depth measurements on the sparsely allocated voxel chunks. The proposed chunk-wise TSDF integration scheme can accurately restore surfaces with superior visual consistency from noisy depth maps and can guarantee the scalability of online reconstruction simultaneously, making our reconstruction framework widely applicable to scenes with various scales and depth scans with strong noises and outliers. The outstanding scalability and efficacy of our ChunkFusion have been corroborated by extensive experiments. To make our results reproducible, the source code is made online available at https://cslinzhang.github.io/ChunkFusion/." @default.
- W4225331314 created "2022-05-05" @default.
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- W4225331314 date "2022-05-23" @default.
- W4225331314 modified "2023-09-27" @default.
- W4225331314 title "Chunkfusion: A Learning-Based RGB-D 3D Reconstruction Framework Via Chunk-Wise Integration" @default.
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- W4225331314 doi "https://doi.org/10.1109/icassp43922.2022.9746993" @default.
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