舔食
发表于 2025-3-23 10:23:28
http://reply.papertrans.cn/24/2343/234249/234249_11.png
precede
发表于 2025-3-23 15:35:57
Gunnar Sohlenius,Leif Clausson,Ann Kjellberg methods learn and predict the complete silhouettes of target instances in 2D space. However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances. With the 2D masks learned, 2D
farewell
发表于 2025-3-23 18:53:27
Use of Constraint Programming for Designthe 2D images counterpart. In this work, we deal with the data scarcity challenge of 3D tasks by transferring knowledge from strong 2D models via RGB-D images. Specifically, we utilize a strong and well-trained semantic segmentation model for 2D images to augment RGB-D images with pseudo-label. The
regale
发表于 2025-3-24 00:31:19
http://reply.papertrans.cn/24/2343/234249/234249_14.png
nerve-sparing
发表于 2025-3-24 04:46:58
http://reply.papertrans.cn/24/2343/234249/234249_15.png
自恋
发表于 2025-3-24 09:53:41
L. Asión-Suñer,I. López-Forniésand shape information of 3D instances. We show that instance kernels enable easy mask inference by simply scanning kernels over the entire scenes, avoiding the heavy reliance on proposals or heuristic clustering algorithms in standard 3D instance segmentation pipelines. The idea of instance kernel i
桉树
发表于 2025-3-24 11:15:39
L. Asión-Suñer,I. López-Forniésalues from known to unknown regions. However, not all natural images have a specifically known foreground. Images of transparent objects, like glass, smoke, web, etc., have less or no known foreground. In this paper, we propose a Transformer-based network, TransMatting, to model transparent objects
exophthalmos
发表于 2025-3-24 15:28:50
http://reply.papertrans.cn/24/2343/234249/234249_18.png
ANIM
发表于 2025-3-24 19:04:56
Advances in Design Engineering IIgnition (.., object detection and panoptic segmentation). Originated from Natural Language Processing (NLP), transformer architectures, consisting of self-attention and cross-attention, effectively learn long-range interactions between elements in a sequence. However, we observe that most existing t
intolerance
发表于 2025-3-25 01:04:38
http://reply.papertrans.cn/24/2343/234249/234249_20.png