作者: Neutropenia 時間: 2025-3-22 00:17
Temporal-Aware Siamese Tracker: Integrate Temporal Context for?3D Object Trackingnt Siamese trackers focus on aggregating the target information from the latest template into the search area for target-specific feature construction, which presents the limited performance in the case of object occlusion or object missing. To this end, in this paper, we propose a novel temporal-aw作者: Affiliation 時間: 2025-3-22 03:53 作者: 西瓜 時間: 2025-3-22 05:26 作者: Override 時間: 2025-3-22 12:20
NEO-3DF: Novel Editing-Oriented 3D Face Creation and?Reconstruction user might wish to edit the reconstructed 3D face, but 3D face editing has seldom been studied. This paper presents such method and shows that reconstruction and editing can help each other. In the presented framework named NEO-3DF, the 3D face model we propose has independent sub-models correspond作者: 燒烤 時間: 2025-3-22 13:54
LSMD-Net: LiDAR-Stereo Fusion with?Mixture Density Network for?Depth Sensinghe stereo camera sensor can provide dense depth prediction but underperforms in texture-less, repetitive and occlusion areas while the LiDAR sensor can generate accurate measurements but results in sparse map. In this paper, we advocate to fuse LiDAR and stereo camera for accurate dense depth sensin作者: 燒烤 時間: 2025-3-22 20:51
Point Cloud Upsampling via?Cascaded Refinement Network by carefully designing a single-stage network, which makes it still challenging to generate a high-fidelity point distribution. Instead, upsampling point cloud in a coarse-to-fine manner is a decent solution. However, existing coarse-to-fine upsampling methods require extra training strategies, whi作者: OATH 時間: 2025-3-22 23:58 作者: 阻擋 時間: 2025-3-23 03:41
Vectorizing Building Blueprintslueprint. A state-of-the-art floorplan vectorization algorithm starts by detecting corners, whose process does not scale to high-definition floorplans with thin interior walls, small door frames, and long exterior walls. Our approach 1) obtains rough semantic segmentation by running off-the-shelf se作者: nitric-oxide 時間: 2025-3-23 06:22 作者: certain 時間: 2025-3-23 10:32 作者: 賠償 時間: 2025-3-23 14:57 作者: 熒光 時間: 2025-3-23 21:55
3D-C2FT: Coarse-to-Fine Transformer for?Multi-view 3D Reconstructionn attention mechanism to explore the multi-view features and exploit their relations for reinforcing the encoding-decoding modules. This paper proposes a new model, namely 3D coarse-to-fine transformer (3D-C2FT), by introducing a novel coarse-to-fine (C2F) attention mechanism for encoding multi-view作者: 抱負 時間: 2025-3-24 00:34
SymmNeRF: Learning to?Explore Symmetry Prior for?Single-View View Synthesishesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by作者: 痛恨 時間: 2025-3-24 05:14
Meta-Det3D: Learn to?Learn Few-Shot 3D Object Detection samples from novel classes for training. Our model has two major components: a . and a .. Given a query 3D point cloud and a few support samples, the 3D meta-detector is trained over different 3D detection tasks to learn task distributions for different object classes and dynamically adapt the 3D o作者: APRON 時間: 2025-3-24 08:02
ReAGFormer: Reaggregation Transformer with?Affine Group Features for?3D Object Detectionm the raw point clouds for 3D object detection, most previous researches utilize PointNet and its variants as the feature learning backbone and have seen encouraging results. However, these methods capture point features independently without modeling the interaction between points, and simple symme作者: 薄荷醇 時間: 2025-3-24 14:01
Training-Free NAS for?3D Point Cloud Processingity of existing networks are relatively fixed, which makes it difficult for them to be flexibly applied to devices with different computational constraints. Instead of manually designing the network structure for each specific device, in this paper, we propose a novel training-free neural architectu作者: indignant 時間: 2025-3-24 18:53
: Optimal Oblivious RAM with?Integrityction scanned blueprint images. Qualitative and quantitative evaluations demonstrate the effectiveness of the approach, making significant boost in standard vectorization metrics over the current state-of-the-art and baseline methods. We will share our code at ..作者: 弄污 時間: 2025-3-24 21:09
Vectorizing Building Blueprintsction scanned blueprint images. Qualitative and quantitative evaluations demonstrate the effectiveness of the approach, making significant boost in standard vectorization metrics over the current state-of-the-art and baseline methods. We will share our code at ..作者: OASIS 時間: 2025-3-25 02:59 作者: nurture 時間: 2025-3-25 07:24
Orr Dunkelman,Nathan Keller,Ariel Weizmannze 3D surface correction in a coarse to fine-grained manner. The proposed model is evaluated by ShapeNet and Multi-view Real-life voxel-based datasets. Experimental results show that 3D-C2FT achieves notable results and outperforms several competing models on these datasets.作者: 責怪 時間: 2025-3-25 09:01 作者: 錢財 時間: 2025-3-25 12:37 作者: 織布機 時間: 2025-3-25 16:53
0302-9743 China, December 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applic作者: Jacket 時間: 2025-3-25 22:54 作者: colony 時間: 2025-3-26 00:51
978-3-031-26318-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 走調 時間: 2025-3-26 04:41 作者: alcoholism 時間: 2025-3-26 09:18 作者: Graphite 時間: 2025-3-26 16:42
Advances in Cryptology – CRYPTO 2022 for a static scene. Capturing all the plenoptic functions in the space of interest is paramount for Image-Based Rendering (IBR) and Novel View Synthesis (NVS). It encodes a complete light-field (., lumigraph) therefore allows one to freely roam in the space and view the scene from any location in a作者: 易于 時間: 2025-3-26 17:07 作者: 填滿 時間: 2025-3-26 22:34 作者: Electrolysis 時間: 2025-3-27 01:50 作者: Keshan-disease 時間: 2025-3-27 07:19
Advances in Cryptology – CRYPTO 2023 by carefully designing a single-stage network, which makes it still challenging to generate a high-fidelity point distribution. Instead, upsampling point cloud in a coarse-to-fine manner is a decent solution. However, existing coarse-to-fine upsampling methods require extra training strategies, whi作者: Geyser 時間: 2025-3-27 09:45
Advances in Cryptology – CRYPTO 2023rom its past observations, ., a continuous sequence of images observed at previous time stamps, and matching them to a large overhead-view satellite image. The critical challenge of this task is to learn a powerful global feature descriptor for the sequential ground-view images while considering its作者: Exuberance 時間: 2025-3-27 17:18
: Optimal Oblivious RAM with?Integritylueprint. A state-of-the-art floorplan vectorization algorithm starts by detecting corners, whose process does not scale to high-definition floorplans with thin interior walls, small door frames, and long exterior walls. Our approach 1) obtains rough semantic segmentation by running off-the-shelf se作者: TAG 時間: 2025-3-27 20:47 作者: Ingrained 時間: 2025-3-27 23:06
Lecture Notes in Computer Scienceproblem. In this paper, we propose a simple yet effective 3D instance segmentation framework that can achieve good performance by annotating only one point for each instance. Specifically, to tackle extremely few labels for instance segmentation, we first oversegment the point cloud into superpoints作者: 護航艦 時間: 2025-3-28 02:56 作者: 重力 時間: 2025-3-28 09:18 作者: indenture 時間: 2025-3-28 11:49
Orr Dunkelman,Nathan Keller,Ariel Weizmannhesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by作者: enterprise 時間: 2025-3-28 16:55 作者: abnegate 時間: 2025-3-28 19:04
Helena Handschuh,Anna Lysyanskayam the raw point clouds for 3D object detection, most previous researches utilize PointNet and its variants as the feature learning backbone and have seen encouraging results. However, these methods capture point features independently without modeling the interaction between points, and simple symme作者: 鋼筆記下懲罰 時間: 2025-3-29 00:23
Arthur Lazzaretti,Charalampos Papamanthouity of existing networks are relatively fixed, which makes it difficult for them to be flexibly applied to devices with different computational constraints. Instead of manually designing the network structure for each specific device, in this paper, we propose a novel training-free neural architectu作者: COW 時間: 2025-3-29 04:18 作者: blister 時間: 2025-3-29 09:27 作者: 思考 時間: 2025-3-29 13:49
EAI-Stereo: Error Aware Iterative Network for?Stereo Matchingwork which could carry more semantic information across iterations. We demonstrate the efficiency and effectiveness of our method on KITTI 2015, Middlebury, and ETH3D. At the time of writing this paper, EAI-Stereo ranks . on the Middlebury leaderboard and . on the ETH3D Stereo benchmark for 50% quan作者: 鳴叫 時間: 2025-3-29 15:32
Temporal-Aware Siamese Tracker: Integrate Temporal Context for?3D Object Trackingate the tracking quality of the template so that the high-quality templates are collected to form the historical template set. Then, with the initial feature embeddings of the historical templates, the temporal feature enhancement module concatenates all template embeddings as a whole and then feeds作者: 退潮 時間: 2025-3-29 23:04
Neural Plenoptic Sampling: Learning Light-Field from?Thousands of?Imaginary Eyesic function at every position in the space of interest. By placing virtual viewpoints (dubbed ‘imaginary eyes’) at thousands of randomly sampled locations and leveraging multi-view geometric relationship, we train the MLP to regress the plenoptic function for the space. Our network is trained on a p作者: 救護車 時間: 2025-3-30 00:32 作者: 香料 時間: 2025-3-30 06:32
NEO-3DF: Novel Editing-Oriented 3D Face Creation and?Reconstruction with the original 2D image. Experiments show that the results of NEO-3DF outperform existing methods in intuitive face editing and have better 3D-to-2D alignment accuracy (14% higher IoU) than global face model-based reconstruction. Code available at ..作者: foliage 時間: 2025-3-30 11:07 作者: Infuriate 時間: 2025-3-30 13:14 作者: 無脊椎 時間: 2025-3-30 17:50 作者: Picks-Disease 時間: 2025-3-30 21:31 作者: 鍍金 時間: 2025-3-31 04:21
Learning Inter-superpoint Affinity for?Weakly Supervised 3D Instance Segmentationplying volume constraints of objects in clustering on the superpoint graph. Extensive experiments on the ScanNet-v2 and S3DIS datasets demonstrate that our method achieves state-of-the-art performance in the weakly supervised point cloud instance segmentation task, and even outperforms some fully su作者: 巨碩 時間: 2025-3-31 06:12 作者: 相互影響 時間: 2025-3-31 12:52
SymmNeRF: Learning to?Explore Symmetry Prior for?Single-View View Synthesisd by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transfor作者: Oafishness 時間: 2025-3-31 15:10
Meta-Det3D: Learn to?Learn Few-Shot 3D Object Detectionmples to characterize the task information, one for each distinct object class. Each re-weighting vector performs channel-wise attention to the candidate features to re-calibrate the query object features, adapting them to detect objects of the same classes. Finally, the adapted features are fed int作者: RAFF 時間: 2025-3-31 20:25
