3D
Model-based Object Recognition and Segmentation in Cluttered
Scenes
Abstract
Viewpoint independent recognition of free-form objects and their
segmentation in the presence of clutter and occlusions is a challenging
task. We present a novel 3D model-based algorithm which performs this
task automatically and efficiently. A 3D model of an object is
automatically constructed offline from its multiple unordered range
images (views). These views are converted into multidimensional table
representations (which we refer to as tensors). Correspondences are
automatically established between these views by simultaneously
matching the tensors of a view with those of the remaining views using
a hash table based voting scheme. This results in a graph of relative
transformations used to register the views before they are integrated
into a seamless 3D model. These models and their tensor representations
constitute the model library. During online
recognition, a tensor from the scene is simultaneously matched with
those in the library by casting votes. Similarity measures are
calculated for the model tensors which receive the most votes. The
model with the highest similarity is transformed to the scene and if it
aligns accurately with an object in the scene, that object is declared
as recognized and is segmented. This process is repeated until the
scene is completely segmented. Experiments were performed on real and
synthetic data comprised of 55 models and 610 scenes and an overall
recognition rate of 95 percent was achieved. Comparison with the spin
images
revealed that our algorithm is superior in terms of recognition rate
and efficiency.
Ajmal Mian, M. Bennamoun and R. Owens, "3D Model-based Object
Recognition and Segmentation in Cluttered Scenes", IEEE
Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 28(10), pp. 1584--1601, 2006.
[pdf]
Copyright ©
IEEE.
On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes
Abstract
Abstract 3D object
recognition from local features is robust to occlusions and clutter.
However, local features must be extracted from a small set of feature
rich keypoints to avoid computational complexity and ambiguous
features. We present an algorithm for the detection of such
keypoints on 3D models and partial views of objects. The keypoints are
highly repeatable between partial views of an object and its complete
3D model. We also propose a quality measure to rank the keypoints and
select the best ones for extracting local features. Keypoints are
identified at locations where a unique local 3D coordinate basis can be
derived from the underlying surface in order to extract invariant
features. We also propose an automatic scale selection technique for
extracting multi-scale and scale invariant features to match objects at
different unknown scales. Features are projected to a PCA subspace and
matched to find correspondences between a database and query object.
Each pair of matching features gives a transformation that aligns the
query and database object. These transformations are clustered and the
biggest cluster is used to identify the query object. Experiments on a
public database revealed that the proposed quality measure relates
correctly to the repeatability of keypoints and the multi-scale
features have a recognition rate of over 95% for up to 80% occluded
objects.
Ajmal Mian, M. Bennamoun
and R. Owens, "On the Repeatability and Quality of Keypoints
for Local Feature-based 3D Object Retrieval from Cluttered Scenes", International Journal of Computer Vision (IJCV), to appear 2009. Copyright ©
Springer Science+Business Media.
Data (along with ground truth) is also available for download
below. Data is provided without any warranty and for research purposes
only. If you use the data, cite the above two papers.
3D Models in PLY format
Chef [download
3.6MB] Parasaurolophus [download
3.6MB] T-rex [download
3.5MB] Chicken [download
2.7MB] Rhino [download
1.6MB]
The above objects were placed in a scene causing
occlusions and clutter. The scene was then scanned with the
Minolta Vivid 910 scanner to get a 2.5D view of the scene. The scenes
are available
for download below. File sizes vary from 1.5 to 2.2 MB.
2.5D Scenes in PLY format (zip files). A snapshot of the first 10 sences is shown rendered with plyview.
NEW! Ground truth locations of the objects and percentage occlusion in the above 50 scenes [download]
