Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling

By Jia Zheng, Junfei Zhang, Jing Li, Rui Tang, Shenghua Gao, Zihan Zhou. 2019

421 citations recorded by the source corpus.

Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often obtained via human labor, which is particularly challenging and inefficient for such tasks due to the large number of 3D structure instances (e.g., line segments) and other factors such as viewpoints and occlusions. In this paper, we present a new synthetic dataset, Structured3D, with the aim of providing large-scale photo-realistic images with rich 3D structure annotations for a wide spectrum of structured 3D modeling tasks. We take advantage of the availability of professional interior designs and automatically extract 3D structures from them. We generate high-quality images with an industry-leading rendering engine. We use our synthetic dataset in combination with real images to train deep networks for room layout estimation and demonstrate improved performance on benchmark datasets.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology, Evaluation & Benchmarks, 3D Vision & Pose

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Citations in the corpus

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (2020, methodology): “…the color image. alistic synthetic datasets and interactive simulation environments that have spurred rapid progress towards the goal of holistic indoor scene understanding [6, 7, 9, 10, 14, 15, 19, 22, 24, 31, 33, 36, 37, 39, 43, 44, 45, 46, 49, 52, 61, 62, 63, 65, 70, 72, 75, 76, 79, 83, 84].”
3D-FUTURE: 3D Furniture Shape with TextURE (2020, methodology): “Previous datasets, such as Structured3D (Zheng et al., 2019) and InteriorNet (Li et al., 2018), also provide realistic indoor images and scene parsing annotations.”
Synthetic Data from Diffusion Models Improves ImageNet Classification (2023, background): “Previous work has explored 3D-rendered datasets [18, 73] and simulation environments with physically realistic engines [11, 15, 16].”
Depth Anything 3: Recovering the Visual Space from Any Views (2025, neutral): “…[98], IRS [93], vKITTI2 [9], BlendedMVS [115], SPRING [56], MVSSynth [38], UnrealStereo4K [123], GTA-SfM [92], TauAgent [27], KenBurns [60], MatrixCity [50], EDEN [47], ReplicaGSO [82], UrbanSyn [32], PointOdyssey [127], Structured3D [125], Objaverse [17], Trel-lis [104], and OmniObject [102].”
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets (2020, background): “[61] diffuse shading Scenenet [39] CGIntrinsic [34] diffuse shading InteriorNet [32] diffuse shading CG-PBR [45] phong shading InvIndoor [33] microfacet envmap 3D-Future [18] AI2-THOR [31] Structure3D [64] shading Hypersim [43] diffuse highlight OpenRooms microfacet envmap al.”
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics (2020, background): “Most recently, Structured3D [49] and OpenRoom [24] have emerged as promising alternatives.”
Geometric Structure Based and Regularized Depth Estimation From 360 Indoor Imagery (2020, methodology): “The images are synthesized with a photorealistic renderer built upon Embree [33], and we use a wellknown path tracing method [25] to achieve realistic rendering, following [38].”