SUN RGB-D: A RGB-D scene understanding benchmark suite

By Shuran Song, Samuel P. Lichtenberg, Jianxiong Xiao. 2015

2,106 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Semantic Understanding of Scenes Through the ADE20K Dataset (2016, methodology): “osoft COCO [18]). Collecting dense annotations where all pixels are labeled is much more challenging. Such efforts include Pascal-Context [22], NYU Depth V2 [23], SUN database [34], SUN RGB-D dataset [31], CityScapes Semantic Understanding of Scenes through the ADE20K Dataset 3 dataset [7], and Open”
The Cityscapes Dataset for Semantic Urban Scene Understanding (2016, background): “Out of the previously discussed datasets, only Cityscapes and KITTI provide instance-level annotations for humans and vehicles.”
Language-driven Semantic Segmentation (2022, background): “We finally train LSeg on a mix of 7 different datasets (Lambert et al., 2020), including ADE20K (Zhou et al., 2019), BDD (Yu et al., 2020), Cityscapes (Cordts et al., 2016), COCO-Panoptic (Lin et al., 2014; Caesar et al., 2018), IDD (Varma et al., 2019), Mapillary Vistas (Neuhold et al., 2017), and ”
Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments (2017, neutral): “Future extensions to the simulator will also support depth image observations (RGB-D), and additional instrumentation in the form of rendered object class and object instance seg-mentations (based on the underlying Matterport3D mesh annotations).”
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (2020, background): “, [65]).”
Scene Parsing through ADE20K Dataset (2017, methodology): “Some of them [19, 1] are evaluated on Pascal-Context [21] or SUN RGB-D dataset [28] to show the capability to segment more object classes in scenes.”
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (2015, methodology): “We evaluate the performance of SegNet on two scene segmentation tasks, CamVid road scene segmentation [22] and SUN RGB-D indoor scene segmentation [23].”
Generative Image Modeling Using Style and Structure Adversarial Networks (2016, methodology): “We compare our method with the discriminator network in DCGAN and the baselines reported in [61]: GIST [64] feature as well as Places-AlexNet [58].”
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data (2019, background): “RGB-D and 3D scene meshes datasets [19, 9, 1, 37, 34] have more objects that are reconstructed along with the scenes, but such objects are often considered in a scene segmentation or object detection task, and not under an object classiﬁcation setup.”
Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection (2021, neutral): “Figure 7 and 8 show the qualitative results of our SDCoT on SUN RGB-D and ScanNet, respectively.”
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, methodology): “However, what makes it particularly valuable for research in scene understanding is its annotation with estimated calibration parameters, camera poses, 3D surface reconstructions, textured meshes, dense object-level semantic segmentations, and aligned CAD models (see Fig.”
Volumetric and Multi-view CNNs for Object Classification on 3D Data (2016, background): “, [26, 31, 28] – this is the first containing hundreds of annotated models from dense 3D reconstructions.”
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images (2015, methodology): “The amodal 3D bounding box are obtained from SUN RGB-D dataset.”
OctNetFusion: Learning Depth Fusion from Data (2017, background): “This has led to the creation of large databases of real-world 3D content [7, 10, 42, 43], enabling progress in a variety of areas including 3D modeling [28, 54], 3D reconstruction [6, 52], 3D recognition [18, 44] and 3D scene understanding [16, 42].”
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation (2016, methodology): “To show the effectiveness of our approach, we carry out comprehensive experiments on seven public datasets, which include six popular datasets for semantic segmentation on indoors and outdoors scenes (NYUDv2, PASCAL VOC 2012, SUN-RGBD, PASCAL-Context, Cityscapes, ADE20K MIT), and one dataset for obj”
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding (2015, background): “SUN RGB-D [35] is a very challenging and large dataset of indoor scenes which is important for domestic robotics.”
"What Happens If..." Learning to Predict the Effect of Forces in Images (2016, methodology): “Our dataset is based on the SUN RGB-D [32] dataset, which contains 10,335 images (we ignore 1,548 images that miss wall annotations or are not fully annotated).”