DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results updated, accepted by ICCV 201

STAT3 in EGF Receptor-Mediated Fibroblast and Human Prostate Cancer Cell Migration, Invasion and Apoptosis.

Growth factor-induced migration is a rate-limiting step in tumor invasiveness. The molecules that regulate this cellular behavior would represent novel targets for limiting tumor cell progression. Epidermal growth factor (EGF) receptor (EGFR)-mediated motility, present in both autocrine and paracrine modes in prostate carcinomas, requires de novo transcription to persist over times greater than a few hours. Therefore, we sought the specific signaling pathways that directly alter cellular transcription. We confirmed that STAT3 directly associates with, and is activated by EGFR in DU-145 and PC3 human prostate carcinoma cells in addition to the model NR6 fibroblast cell line. This correlated with electrophoretic motility shift of STAT3-selective oligonucleotides. Inhibition of STAT3 activity by antisense or siRNA down-regulation or expression of a dominant-negative construct limited cell motility as determined by an in vitro wound healing assay and invasiveness through a matrix barrier. The expression of constitutively activated STAT3 in the absence of EGF did not increase the migration. Together these data indicate that STAT3 is necessary but not sufficient for EGFR-mediated migration. An initial gene array detected a number of candidate operative molecules; the protein levels of both ENA/VASP, a repressor of cell motility, and caspase 3, a nexus of apoptotic signaling, were down regulated by EGF in a STAT3-dependent manner. Preliminary data show that EGF requires STAT3 functioning to inhibit the induction of apoptosis in the two human prostate cancer cell lines. This suggests that STAT3 signaling may be contributing to tumor progression in a second manner by rendering the cells resistant to death. Together, the sum of these findings suggest that STAT3 signaling may be a new target for both limiting prostate tumor cell invasion and enabling the tumor cells to be killed

MOSS: End-to-End Dialog System Framework with Modular Supervision

A major bottleneck in training end-to-end task-oriented dialog system is the lack of data. To utilize limited training data more efficiently, we propose Modular Supervision Network (MOSS), an encoder-decoder training framework that could incorporate supervision from various intermediate dialog system modules including natural language understanding, dialog state tracking, dialog policy learning, and natural language generation. With only 60% of the training data, MOSS-all (i.e., MOSS with supervision from all four dialog modules) outperforms state-of-the-art models on CamRest676. Moreover, introducing modular supervision has even bigger benefits when the dialog task has a more complex dialog state and action space. With only 40% of the training data, MOSS-all outperforms the state-of-the-art model on a complex laptop network troubleshooting dataset, LaptopNetwork, that we introduced. LaptopNetwork consists of conversations between real customers and customer service agents in Chinese. Moreover, MOSS framework can accommodate dialogs that have supervision from different dialog modules at both the framework level and model level. Therefore, MOSS is extremely flexible to update in a real-world deployment

Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection

To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this work, we propose replacing the sparse pseudo-boxes with the dense prediction as a united and straightforward form of pseudo-label. Compared to the pseudo-boxes, our Dense Pseudo-Label (DPL) does not involve any post-processing method, thus retaining richer information. We also introduce a region selection technique to highlight the key information while suppressing the noise carried by dense labels. We name our proposed SS-OD algorithm that leverages the DPL as Dense Teacher. On COCO and VOC, Dense Teacher shows superior performance under various settings compared with the pseudo-box-based methods.Comment: ECCV202

PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures

Neural Radiance Fields (NeRF) have been widely adopted as practical and versatile representations for 3D scenes, facilitating various downstream tasks. However, different architectures, including plain Multi-Layer Perceptron (MLP), Tensors, low-rank Tensors, Hashtables, and their compositions, have their trade-offs. For instance, Hashtables-based representations allow for faster rendering but lack clear geometric meaning, making spatial-relation-aware editing challenging. To address this limitation and maximize the potential of each architecture, we propose Progressive Volume Distillation with Active Learning (PVD-AL), a systematic distillation method that enables any-to-any conversions between different architectures. PVD-AL decomposes each structure into two parts and progressively performs distillation from shallower to deeper volume representation, leveraging effective information retrieved from the rendering process. Additionally, a Three-Levels of active learning technique provides continuous feedback during the distillation process, resulting in high-performance results. Empirical evidence is presented to validate our method on multiple benchmark datasets. For example, PVD-AL can distill an MLP-based model from a Hashtables-based model at a 10~20X faster speed and 0.8dB~2dB higher PSNR than training the NeRF model from scratch. Moreover, PVD-AL permits the fusion of diverse features among distinct structures, enabling models with multiple editing properties and providing a more efficient model to meet real-time requirements. Project website:http://sk-fun.fun/PVD-AL.Comment: Project website: http://sk-fun.fun/PVD-AL. arXiv admin note: substantial text overlap with arXiv:2211.1597