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Claim this profileWorks52 from public data
- Richer Convolutional Features for Edge Detection1,240
RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation, and achieves state-of-the-art performance on several available datasets.
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- NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior378
Experiments on real-world indoor and outdoor scenes show that this method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy.
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- Unsupervised Scale-Consistent Depth Learning from Video226
A monocular depth estimation method SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time and the proposed hybrid Pseudo-RGBD SLAM shows compelling results in KITTI, and it generalizes well to the KAIST dataset without additional training.
- Visual Odometry Revisited: What Should Be Learnt?207
This work revisit the basics of VO and explore the right way for integrating deep learning with epipolar geometry and Perspective-n-Point method and design a simple but robust frame-to-frame VO algorithm (DF-VO) which outperforms pure deep learning-based and geometry-based methods.
- MobileSal: Extremely Efficient RGB-D Salient Object Detection176
This article proposes an implicit depth restoration (IDR) technique to strengthen the mobile networks’ feature representation capability for RGB-D SOD, and proposes compact pyramid refinement (CPR) for efficient multi-level feature aggregation to derive salient objects with clear boundaries.
- SC-DepthV3: Robust Self-Supervised Monocular Depth Estimation for Dynamic Scenes134
This work introduces an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which it proposes novel losses to boost self-supervised training and demonstrates the significantly superior performance of the method over previous methods on six challenging datasets.
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- Semantic Edge Detection with Diverse Deep Supervision113
A novel fully convolutional neural network using diverse deep supervision within a multi-task framework where bottom layers aim at generating category-agnostic edges, while top layers are responsible for the detection of category-aware semantic edges is proposed.
- Auto-Rectify Network for Unsupervised Indoor Depth Estimation89
This work establishes that the complex ego-motions exhibited in handheld settings are a critical obstacle for learning depth, and proposes an Auto-Rectify Network with novel loss functions, which can automatically learn to rectify images during training.
- HFS: Hierarchical Feature Selection for Efficient Image Segmentation69
This paper proposes a real-time system, Hierarchical Feature Selection (HFS), that performs image segmentation at a speed of 50 frames-per-second, and can be used in a variety computer vision tasks that are built on top ofimage segmentation and superpixel extraction.
- DEL: Deep Embedding Learning for Efficient Image Segmentation53
A novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation which can achieve comparable segments when compared with MCG but is much faster than it, i.e. 11.4fps vs. 0.07fps.
- Ordered or Orderless: A Revisit for Video Based Person Re-Identification52
This paper presents a simple yet surprisingly powerful approach for VPRe-id, where it is shown that the common practice of employing recurrent neural networks to aggregate temporal-spatial features may not be optimal and provides an error bound that sheds light upon how could the authors improve VP re-identification.
- Neural Refinement for Absolute Pose Regression with Feature Synthesis49
A test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference and introduces a novel Neural Feature Synthesizer model, which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods.
- When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models49
This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data, and examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue.
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- Diverse Knowledge Distillation for End-to-End Person Search42
This paper proposes a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck of person search and designs a spatial-invariant augmentation to assist model to be invariant to inaccurate detection results.
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- Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue.33
This work establishes that the degenerate camera motions exhibited in handheld settings are a critical obstacle for unsupervised depth learning and proposes a novel data pre-processing method for effective training, i.e., search for image pairs with modest translation and remove their rotation via the proposed weak image rectification.
- Manydepth2: Motion-Aware Self-Supervised Monocular Depth Estimation in Dynamic Scenes30
This paper incorporates optical flow into monocular depth estimation, allowing the model to distinguish between dynamic and static regions in multi-frame inputs, and proposes an attention-based depth network that effectively integrates information from feature maps at different resolutions by incorporating both channel and non-local attention mechanisms.
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- PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction26
The pose residual field (PoRF) is introduced, a novel implicit representation that uses an MLP for regressing pose updates that is more robust than the conventional pose parameter optimisation due to parameter sharing that leverages global information over the entire sequence.
- MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices19
A novel multi-view RGBD dataset captured using a mobile device is introduced, which includes highly precise 3D ground-truth annotations for 153 object models featuring a diverse set of 3D structures.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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