Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligible noise. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned non-uniform noise, while preserving geometric details. Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth. In this paper, we propose a fully convolutional deep autoencoder that learns to denoise depth maps, surpassing the lack of ground truth data. Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised endto-end manner using depth and color information during training, yet only depth during inference. To enforce selfsupervision, we leverage a differentiable rendering technique to exploit photometric supervision, which is further regularized using geometric and surface priors. As the proposed approach relies on raw data acquisition, a large RGB-D corpus is collected using Intel RealSense sensors. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed self-supervised denoising approach on established 3D reconstruction applications. Code is avalable at https://github.com/ VCL3D/DeepDepthDenoising
We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various fullbody movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single-and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra-and inter-sensor HW-SYNC. Moreover, a spatio-temporally aligned scanned and rigged 3D character complements HUMAN4D to enable joint research on time-varying and highquality dynamic meshes. We provide evaluation baselines by benchmarking HUMAN4D with state-of-theart human pose estimation and 3D compression methods. We apply OpenPose and AlphaPose reaching 70.02% and 82.95% mAP PCKh-0.5 on single-and 68.48% and 73.94% mAP PCKh-0.5 on two-person 2D pose estimation, respectively. In 3D pose, a recent multi-view approach named Learnable Triangulation, achieves 80.26% mAP PCK3D-10cm. For 3D compression, we benchmark Draco, Corto and CWIPC open-source 3D codecs, respecting online encoding and steady bit-rates between 7-155 and 2-90 Mbps for mesh-and pointbased volumetric video, respectively. Qualitative and quantitative visual comparison between mesh-based volumetric data reconstructed in different qualities and captured RGB, showcases the available options with respect to 4D representations. HUMAN4D is introduced to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues. The dataset and its code are available online.
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