An interactive approach to semantic modeling of indoor scenes with an RGBD camera

By Tianjia Shao, Weiwei Xu, Kun Zhou, Jingdong Wang, Dongping Li, Baining Guo. 2012

249 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The representative research work using these two datasets as the benchmark for indoor segmentation and classification can be found in [77, 94, 95].”
Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis (2016, methodology): “Another idea is to ﬁnd identical CAD models in a shape database for a given partial input shape and align it with the scan [24, 33, 16, 21, 35].”
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2017, unknown): “One can also ‘complete’ shapes by replacing scanned geometry with aligned CAD models retrieved from a database [20, 32, 15, 17, 33].”
Object detection and classification from large‐scale cluttered indoor scans (2014, methodology): “Indoor reconstruction is an active research topic that has just recently gained a lot of attention in the computer graphics community [KMYG12,NXS12,SXZ ∗ 12].”
Scale Space and Variational Methods in Computer Vision (2015, background): “More recent approaches, such as [12,16] for colour images or [22] for symmetric matrices, rely on the same minimisation but without the restriction to the given data points.”
A Survey of Surface Reconstruction from Point Clouds (2017, unknown): “In other cases we may be concerned with scene reconstruction, and using the database to complement the detected objects in the scene [SXZ ∗ 12,KMHG13].”
Sliding Shapes for 3D Object Detection in Depth Images (2014, background): “3D classification: Classification-based approaches [65–68, 51, 69–79] typically consider the whole object at the same time by extracting a holistic feature for the whole object and classifying the feature vector via a classifier.”
Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve (2020, methodology): “etrieving and aligning similar CAD models. Most methods focus on aligning CAD models to RGB-D scan, point cloud geometry, or 2D-3D surface mapping though various geometric feature matching techniques [1,13,19,21,24,37]. Izadinia and Seitz [20] and Huang et al. [17] propose to optimize for both scene”
3DNN: 3D Nearest Neighbor (2014, methodology): “Researchers are now working on automated methods for inferring the full 3D geometry of a scene given a 2.5D projection (Shao et al. 2012; Silberman et al. 2012).”
Scene Understanding by Reasoning Stability and Safety (2015, background): “…many semantic labeling methods for 3D point clouds play an important role in robotics: Koppula et al. (2011); Anand et al. (2012); Wu et al. (2014), etc; in graphics: e.g., Nan et al. (2012); Shao et al. (2012) (2014); Savva et al. (2014), etc; in 3D shape recognition: Karpathy et al. (2013), etc.”
Inferring 3D Object Pose in RGB-D Images (2015, background): “Finally, [27, 30] among many others, work on the same problem but either consider known instances of objects, or rely on user interaction.”
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation (2016, unknown): “, [29], [30], [31], [32], or part-based models, e.”
3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, methodology): “Given a partial shape input, [25] [29] [36] [45] [47] try to retrieve an identical or most likely CAD model and align it with the partial scan.”
3D Object Dense Reconstruction from a Single Depth View (2018, unknown): “Given a partial shape input, [31] [32] [33] [34] [35] [36] try to retrieve an identical or most likely 3D model and align it with the partial scan.”
Aligning 3D models to RGB-D images of cluttered scenes (2015, background): “Finally, [28, 31], among many others, study the same problem but either consider known instances of objects, or rely on user interaction.”
Scan2CAD: Learning CAD Model Alignment in RGB-D Scans (2018, background): “[36] introduce a semi-automatic system to resolve segmentation ambiguities, where a user first segments a scene into semantic regions, and then shape retrieval is applied.”
3D Semantic Parsing of Large-Scale Indoor Spaces (2016, unknown): “3.2).”