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Sunando Sengupta

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3 papers

1 author row

ICRA Conference 2015 Conference Paper

Semantic octree: Unifying recognition, reconstruction and representation via an octree constrained higher order MRF

Sunando Sengupta
Paul Sturgess

On the one hand, mainly within the computer vision community, multi-resolution image labelling problems with pixel, super-pixel and object levels, have made great progress towards the modelling of holistic scene understanding. On the other hand, mainly within the robotics and graphics communities, multi-resolution 3 D representations of the world have matured to be efficient and accurate. In this paper we bring together the two hands and move towards the new direction of unified recognition, reconstruction and representation. We tackle the problem by embedding an octree into a hierarchical robust P N Markov Random Field. This allows us to jointly infer the multi-resolution 3 D volume along with the object-class labels, all within the constraints of an octree data-structure. The octree representation is chosen as this data-structure is efficient for further processing such as dynamic updates, data compression, and surface reconstruction. We perform experiments in inferring our semantic octree on the The kitti Vision Benchmark Suite in order to demonstrate its efficacy.

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ICRA Conference 2013 Conference Paper

Urban 3D semantic modelling using stereo vision

Sunando Sengupta
Eric Greveson
Ali Shahrokni
Philip H. S. Torr

In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset.

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IROS Conference 2012 Conference Paper

Automatic dense visual semantic mapping from street-level imagery

Sunando Sengupta
Paul Sturgess
Lubor Ladicky
Philip H. S. Torr

This paper describes a method for producing a semantic map from multi-view street-level imagery. We define a semantic map as an overhead, or bird's eye view of a region with associated semantic object labels, such as car, road and pavement. We formulate the problem using two conditional random fields. The first is used to model the semantic image segmentation of the street view imagery treating each image independently. The outputs of this stage are then aggregated over many images to form the input for our semantic map that is a second random field defined over a ground plane. Each image is related by a simple, yet effective, geometrical function that back projects a region from the street view image into the overhead ground plane map. We introduce, and make publicly available, a new dataset created from real world data. Our qualitative evaluation is performed on this data consisting of a 14. 8 km track, and we also quantify our results on a representative subset.

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