One of the main outcomes of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age. Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to improve our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping accuracy was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes. Author summary The question of the complexity of the genetic variants underlying diversity in plant size and shape is central in evolutionary biology to better understand the impacts of selection and adaptation. In this work, we have combined the high resolution of a robotized platform designed to grow Arabidopsis plants under strictly-controlled conditions and the power of quantitative genetics approaches to map the individual genetic components (the 'QTLs') controlling diverse phenotypes, and hence reveal the so-called 'genetic architecture' of these traits. We show that the more we increase our resolution to map QTLs, the more complex of a genetic architecture we reveal. For instance, by focusing all of our mapping power on a small region representing 2.5% of the genome in an unprecedented phenotyping effort, we reveal that several independent QTLs had remained hidden in this region beyond a major-effect QTL that is always clearly visible. If this region is representative of the genome, this means that our current understanding misses potentially hundreds of variants finely controlling traits of evolutionary or agronomical interest.