Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach.
All these works build on top of CNNs pre-trained over ImageNet, for all modal channels. Thus, the very same filters are used to extract features from all of them. As empirically successful as this might be, it is a questionable strategy, as RGB and depth images are perceptually very different, and as such they would benefit from approaches able to learn data-specific features (figure ). Our method matches this challenge, learning RGB features from RGB data and depth features from synthetically generated data, within a deep learning framework. The use of realistic synthetic data in conjunction with deep learning architectures is a promising emerging trend @cite_3 @cite_30 @cite_16 . We are not aware of previous work attempting to use synthetic data to learn depth representations, with or without deep learning techniques.
@cite_30 @cite_16 @cite_3
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In this paper, we explore various aspects of fusing LIDAR and color imagery for pedestrian detection in the context of convolutional neural networks (CNNs), which have recently become state-of-art for many vision problems. We incorporate LIDAR by up-sampling the point cloud to a dense depth map and then extracting three features representing different aspects of the 3D scene. We then use those features as extra image channels. Specifically, we leverage recent work on HHA [9] (horizontal disparity, height above ground, and angle) representations, adapting the code to work on up-sampled LIDAR rather than Microsoft Kinect depth maps. We show, for the first time, that such a representation is applicable to up-sampled LIDAR data, despite its sparsity. Since CNNs learn a deep hierarchy of feature representations, we then explore the question: At what level of representation should we fuse this additional information with the original RGB image channels? We use the KITTI pedestrian detection dataset for our exploration. We first replicate the finding that region-CNNs (R-CNNs) [8] can outperform the original proposal mechanism using only RGB images, but only if fine-tuning is employed. Then, we show that: 1) using HHA features and RGB images performs better than RGB-only, even without any fine-tuning using large RGB web data, 2) fusing RGB and HHA achieves the strongest results if done late, but, under a parameter or computational budget, is best done at the early to middle layers of the hierarchical representation, which tend to represent midlevel features rather than low (e.g. edges) or high (e.g. object class decision) level features, 3) some of the less successful methods have the most parameters, indicating that increased classification accuracy is not simply a function of increased capacity in the neural network. RGB-D images captured by consumer camera can provide pair-wise color and depth information but depth image usually contains strong noise and large holes. Due to different modalities of RGB-D, the intensity-guided depth enhancement easily causes artifacts and blurred edges. To solve this problem, we propose the multi-scale mutual feature convolutional neural network (MSMF-CNN) to learn essential mutual features of RGB-D. This end-to-end framework has two stages, i.e., mutual feature learning and depth regeneration. Firstly, we design two parallel separated subnetworks as intensity and depth mutual feature generator. Specifically, we design a multi-scale mutual feature generator to enforce the depth mutual feature learning and incorporate various structure characteristic. Secondly, depth transformation sub-network combines two mutual features to recover clean depth image. We tested our model on two well-known datasets in terms of PSNR, visual effect and computing speed. Compared with state-of-art algorithms, our method provides better performance of all three criteria. Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data. Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose quartet-net learning to improve the discriminative power of CNN features for instance retrieval. The general idea is to map the features into a space where the image similarity can be better evaluated. Our network differs from the traditional Siamese-net in two ways. First, we adopt a double-margin contrastive loss with a dynamic margin tuning strategy to train the network which leads to more robust performance. Second, we introduce in the mimic learning regularization to improve the generalization ability of the network by preventing it from overfitting to the training data. Catering for the network learning, we collect a large-scale dataset, namely GeoPair, which consists of 68k matching image pairs and 63k non-matching pairs. Experiments on several standard instance retrieval datasets demonstrate the effectiveness of our method.