Shape Anchor Guided Holistic Indoor Scene Understanding
This work addresses the problem of noisy feature grouping and point sampling in indoor scene understanding for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles robust holistic indoor scene understanding by proposing a shape anchor guided learning strategy (AncLearn) to reduce noise in instance detection and mesh reconstruction, achieving state-of-the-art performance on the ScanNetv2 dataset in 3D object detection, layout estimation, and shape reconstruction.
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.