Zobrazeno 1 - 10
of 560
pro vyhledávání: '"Srinivasa, G."'
Autor:
Ghosh, Anurag, Tamburo, Robert, Zheng, Shen, Alvarez-Padilla, Juan R., Zhu, Hailiang, Cardei, Michael, Dunn, Nicholas, Mertz, Christoph, Narasimhan, Srinivasa G.
Perceiving and navigating through work zones is challenging and under-explored, even with major strides in self-driving research. An important reason is the lack of open datasets for developing new algorithms to address this long-tailed scenario. We
Externí odkaz:
http://arxiv.org/abs/2406.07661
Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work, we introduce a novel framework
Externí odkaz:
http://arxiv.org/abs/2403.19022
Driving is challenging in conditions like night, rain, and snow. The lack of good labeled datasets has hampered progress in scene understanding under such conditions. Unsupervised domain adaptation (UDA) using large labeled clear-day datasets is a pr
Externí odkaz:
http://arxiv.org/abs/2403.12712
Publikováno v:
International Symposium on Visual Computing 2023
We propose a novel inverse rendering method that enables the transformation of existing indoor panoramas with new indoor furniture layouts under natural illumination. To achieve this, we captured indoor HDR panoramas along with real-time outdoor hemi
Externí odkaz:
http://arxiv.org/abs/2311.12265
Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges. The absence of precise camera calibration data, including intrinsic and extrinsic parameter
Externí odkaz:
http://arxiv.org/abs/2311.04243
Rain generation algorithms have the potential to improve the generalization of deraining methods and scene understanding in rainy conditions. However, in practice, they produce artifacts and distortions and struggle to control the amount of rain gene
Externí odkaz:
http://arxiv.org/abs/2311.00660
Real-time efficient perception is critical for autonomous navigation and city scale sensing. Orthogonal to architectural improvements, streaming perception approaches have exploited adaptive sampling improving real-time detection performance. In this
Externí odkaz:
http://arxiv.org/abs/2303.14311
Autor:
Virupakshappa Lakkannavar, K.B. Yogesha, C. Durga Prasad, Rakesh Kumar Phanden, Srinivasa G, S Chandrashekar Prasad
Publikováno v:
Results in Surfaces and Interfaces, Vol 16, Iss , Pp 100250- (2024)
In high-temperature boiler operations, conventional steels and alloys grapple with the formidable challenges posed by oxidation and corrosion. Prolonged exposure to extreme thermal conditions exacerbates these issues, progressively diminishing operat
Externí odkaz:
https://doaj.org/article/3780407c6dc543c19abe87d1c007c39c
Near-Periodic Patterns (NPP) are ubiquitous in man-made scenes and are composed of tiled motifs with appearance differences caused by lighting, defects, or design elements. A good NPP representation is useful for many applications including image com
Externí odkaz:
http://arxiv.org/abs/2208.12278
Autor:
Zhi, Tiancheng, Chen, Bowei, Boyadzhiev, Ivaylo, Kang, Sing Bing, Hebert, Martial, Narasimhan, Srinivasa G.
We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatical
Externí odkaz:
http://arxiv.org/abs/2205.13150