We introduce the methodology of explicit model construction to bridge linguistic descriptions and scene perception and demonstrate that in Visual Question-Answering (VQA) using MC4VQA (Model Construction for Visual Question-Answering), a method developed by us. Given a question about a scene, our MC4VQA first recognizes objects utilizing pre-trained deep learning systems. Then, it constructs an explicit 3-D layout by repeatedly reducing the difference between the input scene image and the image rendered from the current 3-D spatial environment. This novel “iterative rendering” process endows MC4VQA the capability of acquiring spatial attributes without training data. MC4VQA outperforms NS-VQA (the SOTA system) by reaching 99.94% accuracy on the benchmark CLEVR datasets, and is more robust than NS-VQA on new testing datasets. With newly created testing data, NS-VQA’s performance dropped to 97.60%, while MC4VQA still kept the 99.0% accuracy. This work sets a new SOTA performance of VQA on the benchmark CLEVR datasets, and shapes a new method that may solve the out-of-distribution problem.