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of 10
pro vyhledávání: '"Lao, Yixing"'
3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results while advancing real-time rendering performance. However, it relies heavily on the quality of the initial point cloud, resulting in blurring and needle-like artifac
Externí odkaz:
http://arxiv.org/abs/2403.15530
Autor:
Tang, Tao, Wang, Guangrun, Lao, Yixing, Chen, Peng, Liu, Jie, Lin, Liang, Yu, Kaicheng, Liang, Xiaodan
Neural implicit fields have been a de facto standard in novel view synthesis. Recently, there exist some methods exploring fusing multiple modalities within a single field, aiming to share implicit features from different modalities to enhance recons
Externí odkaz:
http://arxiv.org/abs/2402.17483
Autor:
Ummenhofer, Benjamin, Agrawal, Sanskar, Sepulveda, Rene, Lao, Yixing, Zhang, Kai, Cheng, Tianhang, Richter, Stephan, Wang, Shenlong, Ros, German
Reconstructing an object from photos and placing it virtually in a new environment goes beyond the standard novel view synthesis task as the appearance of the object has to not only adapt to the novel viewpoint but also to the new lighting conditions
Externí odkaz:
http://arxiv.org/abs/2401.09126
Publikováno v:
NeurIPS 2023
Neural Radiance Fields (NeRFs) have achieved impressive results in novel view synthesis and surface reconstruction tasks. However, their performance suffers under challenging scenarios with sparse input views. We present CorresNeRF, a novel method th
Externí odkaz:
http://arxiv.org/abs/2312.06642
Autor:
Tao, Tang, Gao, Longfei, Wang, Guangrun, Lao, Yixing, Chen, Peng, Zhao, Hengshuang, Hao, Dayang, Liang, Xiaodan, Salzmann, Mathieu, Yu, Kaicheng
We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patter
Externí odkaz:
http://arxiv.org/abs/2304.10406
As a pioneering work exploring transformer architecture for 3D point cloud understanding, Point Transformer achieves impressive results on multiple highly competitive benchmarks. In this work, we analyze the limitations of the Point Transformer and p
Externí odkaz:
http://arxiv.org/abs/2210.05666
We present ASH, a modern and high-performance framework for parallel spatial hashing on GPU. Compared to existing GPU hash map implementations, ASH achieves higher performance, supports richer functionality, and requires fewer lines of code (LoC) whe
Externí odkaz:
http://arxiv.org/abs/2110.00511
Homomorphic encryption (HE)---the ability to perform computation on encrypted data---is an attractive remedy to increasing concerns about data privacy in deep learning (DL). However, building DL models that operate on ciphertext is currently labor-in
Externí odkaz:
http://arxiv.org/abs/1810.10121
Autor:
Cyphers, Scott, Bansal, Arjun K., Bhiwandiwalla, Anahita, Bobba, Jayaram, Brookhart, Matthew, Chakraborty, Avijit, Constable, Will, Convey, Christian, Cook, Leona, Kanawi, Omar, Kimball, Robert, Knight, Jason, Korovaiko, Nikolay, Kumar, Varun, Lao, Yixing, Lishka, Christopher R., Menon, Jaikrishnan, Myers, Jennifer, Narayana, Sandeep Aswath, Procter, Adam, Webb, Tristan J.
The Deep Learning (DL) community sees many novel topologies published each year. Achieving high performance on each new topology remains challenging, as each requires some level of manual effort. This issue is compounded by the proliferation of frame
Externí odkaz:
http://arxiv.org/abs/1801.08058
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