Shape Anchor Guided Holistic Indoor Scene Understanding
Mingyue Dong Linxi Huan Hanjiang Xiong Shuhan Shen Xianwei Zheng
This paper proposes a shape anchor guided learning strategy (AncLearn) for
robust holistic indoor scene understanding. We observe that the search space
constructed by current methods for proposal feature grouping and instance point
sampling often introduces massive noise to instance detection and mesh
reconstruction. Accordingly, we develop AncLearn to generate anchors that
dynamically fit instance surfaces to (i) unmix noise and target-related
features for offering reliable proposals at the detection stage, and (ii)
reduce outliers in object point sampling for directly providing well-structured
geometry priors without segmentation during reconstruction. We embed AncLearn
into a reconstruction-from-detection learning system (AncRec) to generate
high-quality semantic scene models in a purely instance-oriented manner.
Experiments conducted on the challenging ScanNetv2 dataset demonstrate that our
shape anchor-based method consistently achieves state-of-the-art performance in
terms of 3D object detection, layout estimation, and shape reconstruction. The
code will be available at https://github.com/Geo-Tell/AncRec.