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Dellaert, Frank

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Author: Dellaert, Frank × Author: Kundu, Abhijit × Item Type: Publication × search.filters.applied.f.resourcesubtype: Proceedings ×

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Joint Semantic Segmentation and 3D Reconstruction from Monocular Video

2014-09 , Kundu, Abhijit , Li, Yin , Dellaert, Frank , Li, Fuxin , Rehg, James M.

We present an approach for joint inference of 3D scene structure and semantic labeling for monocular video. Starting with monocular image stream, our framework produces a 3D volumetric semantic + occupancy map, which is much more useful than a series of 2D semantic label images or a sparse point cloud produced by traditional semantic segmentation and Structure from Motion(SfM) pipelines respectively. We derive a Conditional Random Field (CRF) model defined in the 3D space, that jointly infers the semantic category and occupancy for each voxel. Such a joint inference in the 3D CRF paves the way for more informed priors and constraints, which is otherwise not possible if solved separately in their traditional frameworks. We make use of class specific semantic cues that constrain the 3D structure in areas, where multiview constraints are weak. Our model comprises of higher order factors, which helps when the depth is unobservable. We also make use of class specific semantic cues to reduce either the degree of such higher order factors, or to approximately model them with unaries if possible. We demonstrate improved 3D structure and temporally consistent semantic segmentation for diffcult, large scale, forward moving monocular image sequence.

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Modern MAP Inference Methods for Accurate and Fast Occupancy Grid Mapping on Higher Order Factor Graphs

2014 , Dhiman, Vikas , Kundu, Abhijit , Dellaert, Frank , Corso, Jason J.

Using the inverse sensor model has been popular in occupancy grid mapping. However, it is widely known that applying the inverse sensor model to mapping requires certain assumptions that are not necessarily true. Even the works that use forward sensor models have relied on methods like expectation maximization or Gibbs sampling which have been succeeded by more effective methods of maximum a posteriori (MAP) inference over graphical models. In this paper, we propose the use of modern MAP inference methods along with the forward sensor model. Our implementation and experimental results demonstrate that these modern inference methods deliver more accurate maps more efficiently than previously used methods.

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