Zobrazeno 1 - 10
of 314
pro vyhledávání: '"Neumann, Ulrich"'
We introduce motion graph, a novel approach to the video prediction problem, which predicts future video frames from limited past data. The motion graph transforms patches of video frames into interconnected graph nodes, to comprehensively describe t
Externí odkaz:
http://arxiv.org/abs/2410.22288
Indoor robots rely on depth to perform tasks like navigation or obstacle detection, and single-image depth estimation is widely used to assist perception. Most indoor single-image depth prediction focuses less on model generalizability to unseen data
Externí odkaz:
http://arxiv.org/abs/2409.02486
Indoor monocular depth estimation helps home automation, including robot navigation or AR/VR for surrounding perception. Most previous methods primarily experiment with the NYUv2 Dataset and concentrate on the overall performance in their evaluation.
Externí odkaz:
http://arxiv.org/abs/2408.13708
Autor:
Gao, Quankai, Xu, Qiangeng, Cao, Zhe, Mildenhall, Ben, Ma, Wenchao, Chen, Le, Tang, Danhang, Neumann, Ulrich
Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly super
Externí odkaz:
http://arxiv.org/abs/2403.12365
Indoor monocular depth estimation has attracted increasing research interest. Most previous works have been focusing on methodology, primarily experimenting with NYU-Depth-V2 (NYUv2) Dataset, and only concentrated on the overall performance over the
Externí odkaz:
http://arxiv.org/abs/2309.13516
A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their appearances across frames. This work introduces an end-to-end trainabl
Externí odkaz:
http://arxiv.org/abs/2308.16154
We propose Strivec, a novel neural representation that models a 3D scene as a radiance field with sparsely distributed and compactly factorized local tensor feature grids. Our approach leverages tensor decomposition, following the recent work TensoRF
Externí odkaz:
http://arxiv.org/abs/2307.13226
Model generalizability to unseen datasets, concerned with in-the-wild robustness, is less studied for indoor single-image depth prediction. We leverage gradient-based meta-learning for higher generalizability on zero-shot cross-dataset inference. Unl
Externí odkaz:
http://arxiv.org/abs/2305.07269
The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in trajectory forecasting with the guidance of high-definition maps, only a few
Externí odkaz:
http://arxiv.org/abs/2207.09646