作者: GREEN 時(shí)間: 2025-3-21 23:36
,Learning-Based Point Cloud Registration for?6D Object Pose Estimation in?the?Real World,is task have shown great success on synthetic datasets, we have observed them to fail in the presence of real-world data. We thus analyze the causes of these failures, which we trace back to the difference between the feature distributions of the source and target point clouds, and the sensitivity o作者: chondromalacia 時(shí)間: 2025-3-22 01:05
,An End-to-End Transformer Model for?Crowd Localization,oxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. In this paper, we propose an elegant, end-to-end .rowd .ocalization .ansformer named CLTR that solves the task in the regression-based paradigm. The proposed method views the crowd localization a作者: 牌帶來(lái) 時(shí)間: 2025-3-22 06:50
,Few-Shot Single-View 3D Reconstruction with?Memory Prior Contrastive Network,revious approaches mainly focus on how to design shape prior models for different categories. Their performance on unseen categories is not very competitive. In this paper, we present a Memory Prior Contrastive Network (MPCN) that can store shape prior knowledge in a few-shot learning based 3D recon作者: Control-Group 時(shí)間: 2025-3-22 09:24 作者: GRATE 時(shí)間: 2025-3-22 13:08 作者: GRATE 時(shí)間: 2025-3-22 21:00 作者: 津貼 時(shí)間: 2025-3-22 21:14 作者: 憤憤不平 時(shí)間: 2025-3-23 05:00 作者: Intercept 時(shí)間: 2025-3-23 06:40 作者: ingenue 時(shí)間: 2025-3-23 11:46 作者: 不透明性 時(shí)間: 2025-3-23 15:14
,PanoFormer: Panorama Transformer for?Indoor 360, Depth Estimation, panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the .rama trans. (named .) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and pano-rama specific metrics. In particular, we divide patches on 作者: regale 時(shí)間: 2025-3-23 20:32
,Self-supervised Human Mesh Recovery with?Cross-Representation Alignment,ted benchmark datasets. Recent progress in self-supervised human mesh recovery has been made using synthetic-data-driven training paradigms where the model is trained from synthetic paired 2D representation (.., 2D keypoints and segmentation masks) and 3D mesh. However, on synthetic dense correspond作者: WAG 時(shí)間: 2025-3-23 23:25 作者: 兩棲動(dòng)物 時(shí)間: 2025-3-24 02:35
,A Reliable Online Method for?Joint Estimation of?Focal Length and?Camera Rotation,online, but these estimates can be unreliable due to irregularities in the scene, uncertainties in line segment estimation and background clutter. Here we address this challenge through four initiatives. First, we use the PanoContext panoramic image dataset?[.] to curate a novel and realistic datase作者: 消滅 時(shí)間: 2025-3-24 06:58 作者: Irritate 時(shí)間: 2025-3-24 14:13 作者: 極端的正確性 時(shí)間: 2025-3-24 16:27 作者: 某人 時(shí)間: 2025-3-24 22:17 作者: palpitate 時(shí)間: 2025-3-24 23:20
0302-9743 tion; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19768-0978-3-031-19769-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Incisor 時(shí)間: 2025-3-25 03:56 作者: impaction 時(shí)間: 2025-3-25 09:37
https://doi.org/10.1007/978-3-662-03161-2 strategy. Our experiments on the LineMOD, LineMOD-Occluded, and T-LESS datasets show that our method yields a significantly better generalization to unseen objects than previous works. Our code and pre-trained models are available at ..作者: 幻影 時(shí)間: 2025-3-25 11:57 作者: 緩和 時(shí)間: 2025-3-25 16:49 作者: 修飾 時(shí)間: 2025-3-25 22:03
Moral Absolutism and Ectopic Pregnancy, reconstructed object can be used for novel-view rendering, relighting, and material editing. Experiments on both synthetic and real datasets demonstrate that our method achieves far more accurate shape reconstruction than existing MVPS and neural rendering methods. Our code and model can be found at ..作者: 直覺(jué)沒(méi)有 時(shí)間: 2025-3-26 01:16
https://doi.org/10.1007/1-4020-3156-4tric learning based approach to accomplish comprehensive enhancement on depth feature by creating a separation between instances in feature space. Extensive experiments and analysis demonstrate the effectiveness of our proposed method. In the end, our method achieves the state-of-the-art performance on KITTI dataset.作者: 上流社會(huì) 時(shí)間: 2025-3-26 07:40
,An End-to-End Transformer Model for?Crowd Localization,ng cost. Extensive experiments conducted on five datasets in various data settings show the effectiveness of our method. In particular, the proposed method achieves the best localization performance on the NWPU-Crowd, UCF-QNRF, and ShanghaiTech Part A datasets.作者: Inculcate 時(shí)間: 2025-3-26 11:46 作者: Palter 時(shí)間: 2025-3-26 14:41
Structural Causal 3D Reconstruction,xplore several approaches to find a task-dependent causal factor ordering. Our experiments demonstrate that the latent space structure indeed serves as an implicit regularization and introduces an inductive bias beneficial for reconstruction.作者: labile 時(shí)間: 2025-3-26 18:56 作者: 好忠告人 時(shí)間: 2025-3-27 00:52
,PS-NeRF: Neural Inverse Rendering for?Multi-view Photometric Stereo, reconstructed object can be used for novel-view rendering, relighting, and material editing. Experiments on both synthetic and real datasets demonstrate that our method achieves far more accurate shape reconstruction than existing MVPS and neural rendering methods. Our code and model can be found at ..作者: ineptitude 時(shí)間: 2025-3-27 04:59 作者: Fretful 時(shí)間: 2025-3-27 07:46 作者: 同義聯(lián)想法 時(shí)間: 2025-3-27 10:53
0302-9743 uter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforceme作者: 信徒 時(shí)間: 2025-3-27 15:51
Conference proceedings 2022n, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learnin作者: bromide 時(shí)間: 2025-3-27 18:16
https://doi.org/10.1007/3-540-28527-Xurst mode to take multiple images within short times. These interesting features lead us to examine depth from focus/defocus. In this work, we present a convolutional neural network-based depth estimation from single focal stacks. Our method differs from relevant state-of-the-art works with three un作者: Aromatic 時(shí)間: 2025-3-27 23:36
Subtropics with year-round rainis task have shown great success on synthetic datasets, we have observed them to fail in the presence of real-world data. We thus analyze the causes of these failures, which we trace back to the difference between the feature distributions of the source and target point clouds, and the sensitivity o作者: 好開(kāi)玩笑 時(shí)間: 2025-3-28 03:25
https://doi.org/10.1007/3-540-28527-Xoxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. In this paper, we propose an elegant, end-to-end .rowd .ocalization .ansformer named CLTR that solves the task in the regression-based paradigm. The proposed method views the crowd localization a作者: FIG 時(shí)間: 2025-3-28 08:19 作者: EVEN 時(shí)間: 2025-3-28 13:48 作者: 蛙鳴聲 時(shí)間: 2025-3-28 17:30
Subtropics with year-round raintive co-teaching framework to distill the learned knowledge from unsupervised teacher networks to a student network. We design an ensemble architecture for our teacher networks, integrating a depth basis decoder with multiple depth coefficient decoders. Depth prediction can then be formulated as a c作者: 在前面 時(shí)間: 2025-3-28 19:41 作者: 改變立場(chǎng) 時(shí)間: 2025-3-28 23:05
https://doi.org/10.1007/978-3-662-03161-2anually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the作者: peritonitis 時(shí)間: 2025-3-29 03:56 作者: bile648 時(shí)間: 2025-3-29 08:38 作者: 和諧 時(shí)間: 2025-3-29 12:37 作者: 圖表證明 時(shí)間: 2025-3-29 16:49
Gregory of Nyssa’s View of the Church panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the .rama trans. (named .) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and pano-rama specific metrics. In particular, we divide patches on 作者: Mortar 時(shí)間: 2025-3-29 22:19 作者: 無(wú)彈性 時(shí)間: 2025-3-30 02:42
Moral Absolutism and Ectopic Pregnancy,ocus on two alternative representations in terms of either parametric meshes or signed distance fields (SDFs). On one side, parametric models can benefit from prior knowledge at the cost of limited shape deformations and mesh resolutions. Mesh models, hence, may fail to precisely reconstruct details作者: 言行自由 時(shí)間: 2025-3-30 04:56
When Does a Human Being Become a Person?,online, but these estimates can be unreliable due to irregularities in the scene, uncertainties in line segment estimation and background clutter. Here we address this challenge through four initiatives. First, we use the PanoContext panoramic image dataset?[.] to curate a novel and realistic datase作者: 元音 時(shí)間: 2025-3-30 09:38 作者: 轉(zhuǎn)向 時(shí)間: 2025-3-30 15:43 作者: 不透明性 時(shí)間: 2025-3-30 20:07
https://doi.org/10.1007/1-4020-3156-4ics. However, since depth estimation and semantic segmentation are fundamentally two types of tasks: one is regression while the other is classification, the distribution of depth feature and semantic feature are naturally different. Previous works that leverage semantic information in depth estimat作者: dapper 時(shí)間: 2025-3-30 21:56
https://doi.org/10.1007/1-4020-3156-4troduces animatable avatars into the capture pipeline for high-fidelity reconstruction in both visible and invisible regions. Our method firstly creates an animatable avatar for the subject from a small number (.20) of 3D scans as a prior. Then given a monocular RGB video of this subject, our method作者: interference 時(shí)間: 2025-3-31 04:51 作者: 謙虛的人 時(shí)間: 2025-3-31 06:32
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234277.jpg作者: patriot 時(shí)間: 2025-3-31 11:32 作者: VERT 時(shí)間: 2025-3-31 15:02 作者: Reverie 時(shí)間: 2025-3-31 17:49 作者: BYRE 時(shí)間: 2025-4-1 01:28 作者: 綠州 時(shí)間: 2025-4-1 02:31 作者: obligation 時(shí)間: 2025-4-1 09:58 作者: 斗爭(zhēng) 時(shí)間: 2025-4-1 10:51
,Adaptive Co-teaching for?Unsupervised Monocular Depth Estimation,ent, which effectively improves the ability of the student to jump out of the local minimum. Our method is shown to significantly benefit unsupervised depth estimation and sets new state of the art on both KITTI and Nuscenes datasets.作者: Dedication 時(shí)間: 2025-4-1 16:25
Lidar Point Cloud Guided Monocular 3D Object Detection,3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measuremen作者: Estimable 時(shí)間: 2025-4-1 21:15 作者: 美食家 時(shí)間: 2025-4-1 23:38
,PanoFormer: Panorama Transformer for?Indoor 360, Depth Estimation,ents demonstrate that our approach significantly outperforms the state-of-the-art (SOTA) methods. Furthermore, the proposed method can be effectively extended to solve semantic panorama segmentation, a similar pixel2pixel task.
