Zobrazeno 1 - 10
of 656
pro vyhledávání: '"Wang, Jiaxu"'
Rendering high-fidelity images from sparse point clouds is still challenging. Existing learning-based approaches suffer from either hole artifacts, missing details, or expensive computations. In this paper, we propose a novel framework to render high
Externí odkaz:
http://arxiv.org/abs/2407.03857
Autor:
Wang, Jiaxu, Zhang, Ziyi, Zhang, Qiang, Li, Jia, Sun, Jingkai, Sun, Mingyuan, He, Junhao, Xu, Renjing
Latent scene representation plays a significant role in training reinforcement learning (RL) agents. To obtain good latent vectors describing the scenes, recent works incorporate the 3D-aware latent-conditioned NeRF pipeline into scene representation
Externí odkaz:
http://arxiv.org/abs/2406.02370
Autor:
Cao, Jiahang, Zhang, Qiang, Wang, Ziqing, Wang, Jiaxu, Cheng, Hao, Shao, Yecheng, Zhao, Wen, Han, Gang, Guo, Yijie, Xu, Renjing
Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, achieving significant success. However, RL trajectories possess unique
Externí odkaz:
http://arxiv.org/abs/2406.02013
Event cameras offer promising advantages such as high dynamic range and low latency, making them well-suited for challenging lighting conditions and fast-moving scenarios. However, reconstructing 3D scenes from raw event streams is difficult because
Externí odkaz:
http://arxiv.org/abs/2405.14959
An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment representation directly influences the achievement of the learning task. Previ
Externí odkaz:
http://arxiv.org/abs/2404.07950
Event camera, a novel bio-inspired vision sensor, has drawn a lot of attention for its low latency, low power consumption, and high dynamic range. Currently, overfitting remains a critical problem in event-based classification tasks for Spiking Neura
Externí odkaz:
http://arxiv.org/abs/2403.09274
3D neural implicit representations play a significant component in many robotic applications. However, reconstructing neural radiance fields (NeRF) from realistic event data remains a challenge due to the sparsities and the lack of information when o
Externí odkaz:
http://arxiv.org/abs/2401.17121
This paper introduces a novel paradigm for the generalizable neural radiance field (NeRF). Previous generic NeRF methods combine multiview stereo techniques with image-based neural rendering for generalization, yielding impressive results, while suff
Externí odkaz:
http://arxiv.org/abs/2401.14354
The ability to detect objects in all lighting (i.e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving.Traditional RGB-based detectors often fail under such varying lighting conditions.Therefor
Externí odkaz:
http://arxiv.org/abs/2309.09297
Publikováno v:
Shipin Kexue, Vol 44, Iss 22, Pp 64-73 (2023)
Malatya cheeses with different cow to goat milk ratios (1:0, 3:1, 1:1, 1:3 and 0:1) were prepared to investigate the effect of blend ratio between cow and goat milk on the yield, physicochemical properties, texture, color, melting temperature, sensor
Externí odkaz:
https://doaj.org/article/ff9906a7adb44cfa935401f7c6b5f0a4