Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB

• URL: http://arxiv.org/abs/2409.11104v1
• Date: Tue, 17 Sep 2024 11:59:34 GMT
• Title: Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB
• Authors: Alessandro Simoni, Francesco Marchetti, Guido Borghi, Federico Becattini, Davide Davoli, Lorenzo Garattoni, Gianpiero Francesca, Lorenzo Seidenari, Roberto Vezzani,
• Abstract summary: Heatmap-based 3D pose estimator is able to hallucinate depth information from the RGB frames given at inference time. depth information is used exclusively during training by enforcing our RGB-based hallucination network to learn similar features to a backbone pre-trained only on depth data.
• Score: 48.31210455404533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the same 2D projection on the image. In this context, depth data could help to disambiguate the 2D information by providing additional constraints about the distance between objects in the scene and the camera. Unfortunately, the acquisition of accurate depth data is limited to indoor spaces and usually is tied to specific depth technologies and devices, thus limiting generalization capabilities. In this paper, we propose a method able to leverage the benefits of depth information without compromising its broader applicability and adaptability in a predominantly RGB-camera-centric landscape. Our approach consists of a heatmap-based 3D pose estimator that, leveraging the paradigm of Privileged Information, is able to hallucinate depth information from the RGB frames given at inference time. More precisely, depth information is used exclusively during training by enforcing our RGB-based hallucination network to learn similar features to a backbone pre-trained only on depth data. This approach proves to be effective even when dealing with limited and small datasets. Experimental results reveal that the paradigm of Privileged Information significantly enhances the model's performance, enabling efficient extraction of depth information by using only RGB images.

Related papers

• Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis [48.59382455101753]
  2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining.
  arXiv  Detail & Related papers  (2024-03-11T09:12:24Z)
• RBF Weighted Hyper-Involution for RGB-D Object Detection [0.0]
  We propose a real-time and two stream RGBD object detection model. The proposed model consists of two new components: a depth guided hyper-involution that adapts dynamically based on the spatial interaction pattern in the raw depth map and an up-sampling based trainable fusion layer. We show that the proposed model outperforms other RGB-D based object detection models on NYU Depth v2 dataset and achieves comparable (second best) results on SUN RGB-D.
  arXiv  Detail & Related papers  (2023-09-30T11:25:34Z)
• 3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation [50.15926681475939]
  Inverse graphics aims to infer the 3D scene structure from 2D images. We introduce probabilistic modeling to quantify uncertainty and achieve robustness in 6D pose estimation tasks. 3DNEL effectively combines learned neural embeddings from RGB with depth information to improve robustness in sim-to-real 6D object pose estimation from RGB-D images.
  arXiv  Detail & Related papers  (2023-02-07T20:48:35Z)
• Monocular Depth Estimation Using Cues Inspired by Biological Vision Systems [22.539300644593936]
  Monocular depth estimation (MDE) aims to transform an RGB image of a scene into a pixelwise depth map from the same camera view. Part of the MDE task is to learn which visual cues in the image can be used for depth estimation, and how. We demonstrate that explicitly injecting visual cue information into the model is beneficial for depth estimation.
  arXiv  Detail & Related papers  (2022-04-21T19:42:36Z)
• Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction [91.43066633305662]
  We propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD) Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time.
  arXiv  Detail & Related papers  (2022-03-09T17:20:18Z)
• RGB-D Salient Object Detection with Ubiquitous Target Awareness [37.6726410843724]
  We make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. We propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task. Our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS.
  arXiv  Detail & Related papers  (2021-09-08T04:27:29Z)
• Is Depth Really Necessary for Salient Object Detection? [50.10888549190576]
  We make the first attempt in realizing an unified depth-aware framework with only RGB information as input for inference. Not only surpasses the state-of-the-art performances on five public RGB SOD benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a large margin.
  arXiv  Detail & Related papers  (2020-05-30T13:40:03Z)
• Single Image Depth Estimation Trained via Depth from Defocus Cues [105.67073923825842]
  Estimating depth from a single RGB image is a fundamental task in computer vision. In this work, we rely, instead of different views, on depth from focus cues. We present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches.
  arXiv  Detail & Related papers  (2020-01-14T20:22:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.