Deep semantic hashing of 3D geometric features for efficient 3D model retrieval

As the scale of 3D model databases increase, speed, in addition to accuracy, of its search becomes very important. One way to achieve fast search is to use a compact 3D shape feature whose cost of comparison is very small. Binarization of a real-valued feature vector, via hashing, is a way to obtain such compact feature vector. Previous algorithms for producing binarized 3D model features via hashing consisted of two disconnected stages; handcrafted real-valued 3D shape feature extraction followed by hashing into binary code. This compartmentalized approach, however, leads to less-than optimal binary codes, as these two stages are optimized independently. This paper proposes a deep semantic hashing algorithm called Binarized Deep Local feature Aggregation Network (BDLAN) which jointly optimizes real-valued feature extraction per 3D model and its banarization via hashing. BDLAN training minimizes quantization error caused by binarization. However, this constraint alone often maps real-valued features to their nearest binary codes in Hamming space, which are nonoptimal local minima. To alleviate the issue, we add a simple regularization called Probabilistic Bit Inversion (PBI) of binary codes. Experimental evaluation of the proposed algorithms demonstrates superior efficiency and competitive accuracy to the existing 3D model retrieval algorithms employing real-valued features.