certain
发表于 2025-3-23 10:32:21
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赔偿
发表于 2025-3-23 14:57:31
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荧光
发表于 2025-3-23 21:55:56
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
抱负
发表于 2025-3-24 00:34:22
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
痛恨
发表于 2025-3-24 05:14:18
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
APRON
发表于 2025-3-24 08:02:18
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
薄荷醇
发表于 2025-3-24 14:01:12
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
indignant
发表于 2025-3-24 18:53:14
: 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 ..
弄污
发表于 2025-3-24 21:09:22
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 ..
OASIS
发表于 2025-3-25 02:59:17
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