The objective of this proposal is to increase the range resolution of time-of-flight (ToF) cameras by utilizing correlation among multi-frame data through projection onto convex sets (POCS) technique. Our work aims to produce a super-resolution depth map and reduce the depth error. A calibration based technique is proposed to form constraint sets that will compensate for spatially varying depth errors within the work volume. Our proposed framework incorporates adaptive kernels along with novel constraint sets to form high resolution depth maps. ToF cameras have the capability of producing real-time depth maps, reaching nearly up to 100fps. They are also compact in the sense that no moving gimbals or extra parts such as mirros and scanners are required for data acquisition. One of the application areas in which ToF cameras are used is 3D scene modeling which has matured to great extent during last decade. In addition to that, high-end automobile industry, production lines, and biomedical inspection are some of the areas in which range sensing is applied. The purpose of our proposal is to provide a way for ToF cameras to realize their potential for range sensing by increasing their depth resolution.