Due to growing environmental awareness, the Circular Economy and in particular the concept of ReverseLogistics (RL) are more and more becoming the focus of industry, yielding ecological as well as economicadvantages. However, the successful implementation of the concepts requires that several challenges be met.One of the most common challenges is the lack of information within RL. One proposed solution is to usemore Automatic Identification Systems (Auto-ID) to track returning goods and close the information gapsbetween RL participants. Currently available identification systems are often limited in their field ofapplication, as they can be very expensive, require a huge change in current logistics processes or sufferfrom physical characteristics, such as electromagnetic absorption or limited visual contact. With this paper,we introduce our novel concept for load carrier classification and quantification using Time-of-Flight (ToF)cameras in combination with color images, beginning with a general overview of the system architectureand process structure. This is followed by an in-depth analysis of the process steps, starting with triggeringcamera records followed by image pre-processing, classification and finally a quantification of the loadedcargo.