Sur²f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images

Zhangjin Huang*, Zhihao Liang*, Haojie Zhang, Yangkai Lin, Kui Jia

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• We propose a new hybrid representation, termed Sur²f, that can enjoy the benefits of both explicit and implicit surface representations. This is achieved by learning two parallel streams of an implicit SDF and an explicit surrogate surface mesh, both of which, by rendering, receive supervision from multi-view image observations.

HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization

Zhihao Liang*, Zhangjin Huang*, Changxing Ding, Kui Jia

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• We present a novel method of HelixSurf for reconstruction of indoor scene surface from multi-view images. HelixSurf enjoys the complementary benefits of the traditional MVS and the recent neural implicit surface learning, by regularizing the learning/optimization of one strategy iteratively using the intermediate prediction from the other.

Surface Reconstruction from Point Clouds: A Survey and a Benchmark

Zhangjin Huang*, Yuxin Wen*, Zihao Wang, Jinjuan Ren, Kui Jia

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• We review and benchmark existing methods in the new era of deep learning surface reconstruction. We contribute a large-scale benchmarking dataset consisting of both synthetic and real-scanned data; the benchmark includes object- and scene-level surfaces and takes into account various sensing imperfections that are commonly encountered in practical depth scanning. We conduct thorough empirical studies by comparing existing methods on the constructed benchmark. Our studies help identity the best conditions under which different methods work, and suggest some empirical findings.