Zobrazeno 1 - 10
of 833
pro vyhledávání: '"FAN Xiaopeng"'
Reconstructing desired objects and scenes has long been a primary goal in 3D computer vision. Single-view point cloud reconstruction has become a popular technique due to its low cost and accurate results. However, single-view reconstruction methods
Externí odkaz:
http://arxiv.org/abs/2412.09055
The common occurrence of occlusion-induced incompleteness in point clouds has made point cloud completion (PCC) a highly-concerned task in the field of geometric processing. Existing PCC methods typically produce complete point clouds from partial po
Externí odkaz:
http://arxiv.org/abs/2412.08326
Recent 4D reconstruction methods have yielded impressive results but rely on sharp videos as supervision. However, motion blur often occurs in videos due to camera shake and object movement, while existing methods render blurry results when using suc
Externí odkaz:
http://arxiv.org/abs/2412.06424
Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two independent Artificial Neural Network (ANN) branches, limiting cross-modality
Externí odkaz:
http://arxiv.org/abs/2411.18658
Due to the challenges in acquiring paired Text-3D data and the inherent irregularity of 3D data structures, combined representation learning of 3D point clouds and text remains unexplored. In this paper, we propose a novel Riemann-based Multi-scale A
Externí odkaz:
http://arxiv.org/abs/2408.13712
Text-driven 3D scene generation has seen significant advancements recently. However, most existing methods generate single-view images using generative models and then stitch them together in 3D space. This independent generation for each view often
Externí odkaz:
http://arxiv.org/abs/2408.13711
Multi-modal crowd counting is a crucial task that uses multi-modal cues to estimate the number of people in crowded scenes. To overcome the gap between different modalities, we propose a modal emulation-based two-pass multi-modal crowd-counting frame
Externí odkaz:
http://arxiv.org/abs/2407.19491
The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown great potential in extracting audio-visual joint feature representations. However, coupling SNNs (binary spike sequences) with transformers (float-point sequence
Externí odkaz:
http://arxiv.org/abs/2407.08130
Place recognition is a fundamental task for robotic application, allowing robots to perform loop closure detection within simultaneous localization and mapping (SLAM), and achieve relocalization on prior maps. Current range image-based networks use s
Externí odkaz:
http://arxiv.org/abs/2405.10793
Autor:
Zhou, Chenlin, Zhang, Han, Yu, Liutao, Ye, Yumin, Zhou, Zhaokun, Huang, Liwei, Ma, Zhengyu, Fan, Xiaopeng, Zhou, Huihui, Tian, Yonghong
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training alg
Externí odkaz:
http://arxiv.org/abs/2405.04289