Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic

certain

賠償

熒光

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

抱負(fù)

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

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

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

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

: 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

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