Re: [eigen] Transform products |

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*To*: eigen@xxxxxxxxxxxxxxxxxxx*Subject*: Re: [eigen] Transform products*From*: Gael Guennebaud <gael.guennebaud@xxxxxxxxx>*Date*: Thu, 19 Feb 2009 20:45:39 +0100*Dkim-signature*: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=gamma; h=domainkey-signature:mime-version:received:in-reply-to:references :date:message-id:subject:from:to:content-type :content-transfer-encoding; bh=DrUtGEQvTEMPM2CG7GvYjC5UW2FuAVgJTlgcIsDpxio=; b=LeajsmzJCGMHS3YCEY30Cv5wH7rHR1+J6UckG21CDUg9JmYzQfFo3P3jg+d9ES+/mz cIv25g01k6i4EgvaIEroaCgSQqnVdgVN5+pbdMujEpxXF8YJiEjTrVKpbTCIvU0KW5is 5xE9RNV1JEwoWiQDLWlT54b5SJSf4ngpJvCvM=*Domainkey-signature*: a=rsa-sha1; c=nofws; d=gmail.com; s=gamma; h=mime-version:in-reply-to:references:date:message-id:subject:from:to :content-type:content-transfer-encoding; b=aNdCYiqjInhzFjQnzMavmciTBKw2f3Kn4EsT8Fk3u1Oifup2SmNcXbfijxtpG3Myd0 JJwjWnWmhmt0rb2Vsk96Q/G1SU1e+bphHpMLrGirQMsep4iZ/ZCNB6WwIuh/Oea5BAlZ zslC8lb76d7WSarDYYjj5UdEyrR2TllL47o58=

hm, yes for all kind of projecting mappings. Now of course for such use cases most of the Transform's features are not very useful. What's useful is the automatic homogeneous normalization. But I agree, that's not a common use case, and so I agree to assume an affine transformation as default, excepted in two cases: 1 - the user provide homogeneous vectors and so we return homogeneous vectors: Transform * [d+1 x N] => [d+1 x N] (here "t * rhs" is just a shortcut for "t.matrix() * rhs") 2 - when using the future special "projective product": t.projectiveTransform( [d x N] ) what do you think ? btw, recent OpenGL can deal with 3x4 (only with shaders) and/or row major matrices. On Thu, Feb 19, 2009 at 3:20 PM, Benoit Jacob <jacob.benoit.1@xxxxxxxxx> wrote: > Hi, > > I think the following question needs to be adressed first: couldn't we > just assume that the last row is 0,...,0,1 ? Is there really any use > case for non-affine transformations? Thus the last row would be there > just for seamless interoperability with OpenGL. > > Or do you have a real use case where the last row is not 0,...,0,1 ? > > Cheers, > Benoit > > 2009/2/19 Gael Guennebaud <gael.guennebaud@xxxxxxxxx>: >> Hi list, >> >> there still remains a few issues with the product: Transform * matrix_expression >> >> Let d be the dimension of the ambient space (so that the Transform >> object actually correspond to a d+1 x d+1 matrix). Currently we allow: >> >> 1 - Transform * [d x d] => Transform >> 2 - Transform * [d+1 x d+1] => trivial product expression >> 3 - Transform * [d+1 x 1] => trivial product expression >> 4 - Transform * [d x 1] => complex [d x 1] expression including the >> homogeneous normalization >> >> Issues: >> >> a) the 4-th case is not plenty satisfactory: >> >> a1 - should it returns an homogeneous vector ? >> a2 - or automatically does the normalization as it currently does ? >> a3 - or should we offer a way to skip the normalization assuming the >> transformation is affine (last row = [0 ... 0 1]) ? >> >> Well, these questions are more complementary and I guess the answer is >> yes for all, the problem is rather how to expose all these variants ? >> a proposal: >> >> for a3 let's add "t.affine() * v" where affine() would return a >> kind of [d x d+1] proxy with overloaded operator *. >> >> for a1 and a2, two options: >> >> p1) keep the default as it because it is safe >> and for a1... well I don't know, anyway the user can still >> build and homogeneous one for the rhs. >> >> p2) let's return a "homogeneous" object which would >> automatically be converted to a [d x 1] vector if needed >> (ideally would have to be done in MatrixBase) >> >> >> >> b) second issue: We want to be able to perform a batch transformation >> of a set of N vectors. Again two cases: >> >> b1 - Transform * [d+1 x N] => this is trivial, we just have to merge >> the above cases 1 and 2 into a more generic one. DONE >> >> b2 - Transform * [d x N] => same issues than the ones discussed >> above excepted that the involved expressions are much more complicated >> ! For instance the affine case would be: >> t.matrix().linear() * [d x N] + t.matrix().translation() * >> Matrix<Scalar,d,N>::Ones(N); >> >> This last example also shows that maybe it would be useful to have a >> "replicate" expression mapping a vector to a matrix with constant rows >> or constant columns ? This would be simpler than using a matrix >> product for that. >> >> And of course all the above discussion also holds for the transpose >> cases, i.e., matrix_expression * Transform. >> >> >> opinion, idea ? >> >> thanks, >> Gael. >> >> >> > > >

**Follow-Ups**:**Re: [eigen] Transform products***From:*Keir Mierle

**Re: [eigen] Transform products***From:*Benoit Jacob

**References**:**[eigen] Transform products***From:*Gael Guennebaud

**Re: [eigen] Transform products***From:*Benoit Jacob

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