<p>I can advise you to use <a href="http://www.robots.ox.ac.uk/%7Evgg/research/affine/det_eval_files/mikolajczyk_ijcv2004.pdf" rel="nofollow noreferrer">Hessian-Affine</a> and <a href="http://www.robots.ox.ac.uk/%7Evgg/research/affine/det_eval_files/matas_bmvc2002.pdf" rel="nofollow noreferrer">MSER</a> for detection, if you need invariance to different factors (e.g., viewpoint change) or FAST, if you need real time.
FAST is doing similar job to the Harris, but much faster.</p>
<p>You can look into &quot;<a href="http://epubs.surrey.ac.uk/726872/1/Tuytelaars-FGV-2008.pdf" rel="nofollow noreferrer">Local Invariant Feature Detectors: A Survey</a>&quot;, and &quot;<a href="http://www.robots.ox.ac.uk/%7Evgg/research/affine/det_eval_files/vibes_ijcv2004.pdf" rel="nofollow noreferrer">A Comparison of Affine Region Detectors</a>&quot; where many detectors are tested and described very well.</p>
<p><strong>Update:</strong> &quot;<a href="http://arxiv.org/abs/1504.06603" rel="nofollow noreferrer">WxBS: Wide Baseline Stereo Generalizations</a>&quot; does extended benchmark of the novel and classical detectors and descriptors.</p>
<p>Second, the description part is usually slower than detection, so to be real-time you have to use GPU or binary descriptor like <a href="http://cvlabwww.epfl.ch/%7Elepetit/papers/calonder_eccv10.pdf" rel="nofollow noreferrer">BRIEF</a> or <a href="http://www.ivpe.com/freak.htm" rel="nofollow noreferrer">FREAK</a>.</p>
<p><strong>Update2:</strong> &quot;<a href="https://github.com/featw/hbench" rel="nofollow noreferrer">HPatches (Homography Patches) dataset and benchmark</a>&quot;  and corresponding workshop at ECCV 2016. <a href="http://www.iis.ee.ic.ac.uk/ComputerVision/DescrWorkshop/index.html" rel="nofollow noreferrer">http://www.iis.ee.ic.ac.uk/ComputerVision/DescrWorkshop/index.html</a> .</p>
<p><strong>Update3:</strong> &quot;<a href="https://cvg.ethz.ch/research/local-feature-evaluation/" rel="nofollow noreferrer">Comparative Evaluation of Hand-Crafted and Learned Local Features</a>&quot;  Descriptors (and a bit detectors) evaluation on large-scale 3D reconstruction task CVPR 2017 .</p>
<p><strong>Update4:</strong> &quot;<a href="https://arxiv.org/abs/1809.11039" rel="nofollow noreferrer">Interest point detectors stability evaluation on ApolloScape dataset</a>&quot;  Detector evaluation on authonomous driving dataset, ECCVW2018 .</p>
<p><strong>Update5:</strong> &quot;<a href="https://arxiv.org/abs/1807.10254" rel="nofollow noreferrer">From handcrafted to deep local invariant features</a>&quot; Huuuge survey-overview paper about handcrafted and learned features, 2018.</p>
<p><strong>Update6:</strong> &quot;<a href="https://arxiv.org/abs/2003.01587" rel="nofollow noreferrer">Image Matching across Wide Baselines: From Paper to Practice</a>&quot; Large scale benchmark of the abovementioned and more recent methods for the camera pose estimation. IJCV, 2020.</p>
feature detectors and descriptors comparison
<p>There are several kinds of detectors and descriptors, like SIFT, SURF, FAST. I wonder are they all eligible for real-time applications? Which is the best or better?</p>

<p>And furthermore, is Harris-Laplacian dectector still useful when we already have the above three? Is it better than them?</p>