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3d Scenes

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Dieter Fox – One of the best experts on this subject based on the ideXlab platform.

  • detection based object labeling in 3d Scenes
    International Conference on Robotics and Automation, 2012
    Co-Authors: Kevin Lai, Xiaofeng Ren, Dieter Fox

    Abstract:

    We propose a view-based approach for labeling objects in 3d Scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3d scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3d shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.

  • ICRA – Detection-based object labeling in 3d Scenes
    2012 IEEE International Conference on Robotics and Automation, 2012
    Co-Authors: Kevin Lai, Xiaofeng Ren, Dieter Fox

    Abstract:

    We propose a view-based approach for labeling objects in 3d Scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3d scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3d shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.

Hao Zhang – One of the best experts on this subject based on the ideXlab platform.

  • language driven synthesis of 3d Scenes from scene databases
    ACM Transactions on Graphics, 2019
    Co-Authors: Akshay Gadi Patil, Matthew Fisher, Soren Pirk, Binhson Hua, Saikit Yeung, Xin Tong, Leonidas J Guibas, Hao Zhang

    Abstract:

    We introduce a novel framework for using natural language to generate and edit 3d indoor Scenes, harnessing scene semantics and text-scene grounding knowledge learned from large annotated 3d scene databases. The advantage of natural language editing interfaces is strongest when performing semantic operations at the sub-scene level, acting on groups of objects. We learn how to manipulate these sub-Scenes by analyzing existing 3d Scenes. We perform edits by first parsing a natural language command from the user and transforming it into a semantic scene graph that is used to retrieve corresponding sub-Scenes from the databases that match the command. We then augment this retrieved sub-scene by incorporating other objects that may be implied by the scene context. Finally, a new 3d scene is synthesized by aligning the augmented sub-scene with the user’s current scene, where new objects are spliced into the environment, possibly triggering appropriate adjustments to the existing scene arrangement. A suggestive modeling interface with multiple interpretations of user commands is used to alleviate ambiguities in natural language. We conduct studies comparing our approach against both prior text-to-scene work and artist-made Scenes and find that our method significantly outperforms prior work and is comparable to handmade Scenes even when complex and varied natural sentences are used.

  • Fully computed holographic stereogram based algorithm for computer-generated holograms with accurate depth cues
    Optics Express, 2015
    Co-Authors: Hao Zhang, Yan Zhao, Liangcai Cao, Guofan Jin

    Abstract:

    We propose an algorithm based on fully computed holographic stereogram for calculating full-parallax computer-generated holograms (CGHs) with accurate depth cues. The proposed method integrates point source algorithm and holographic stereogram based algorithm to reconstruct the three-dimensional (3d) Scenes. Precise accommodation cue and occlusion effect can be created, and computer graphics rendering techniques can be employed in the CGH generation to enhance the image fidelity. Optical experiments have been performed using a spatial light modulator (SLM) and a fabricated high-resolution hologram, the results show that our proposed algorithm can perform quality reconstructions of 3d Scenes with arbitrary depth information.

Kevin Lai – One of the best experts on this subject based on the ideXlab platform.

  • detection based object labeling in 3d Scenes
    International Conference on Robotics and Automation, 2012
    Co-Authors: Kevin Lai, Xiaofeng Ren, Dieter Fox

    Abstract:

    We propose a view-based approach for labeling objects in 3d Scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3d scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3d shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.

  • ICRA – Detection-based object labeling in 3d Scenes
    2012 IEEE International Conference on Robotics and Automation, 2012
    Co-Authors: Kevin Lai, Xiaofeng Ren, Dieter Fox

    Abstract:

    We propose a view-based approach for labeling objects in 3d Scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3d scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3d shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.