Search Results for author:
Found 27 papers, 11 papers with code
Higher-Order CRF Structural Segmentation of 3D Reconstructed Surfaces
In this paper, we propose a structural segmentation algorithm to partition multi-view stereo reconstructed surfaces of large-scale urban environments into structural segments.
Parallel Structure from Motion from Local Increment to Global Averaging
In this paper, we tackle the accurate and consistent Structure from Motion (SfM) problem, in particular camera registration, far exceeding the memory of a single computer in parallel.
Progressive Large Scale-Invariant Image Matching in Scale Space
In this paper, we propose a scale-invariant image matching approach to tackling the very large scale variation of views.
MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image
In this paper, we address the problem of reconstructing an object's surface from a single image using generative networks.
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension.
Ranked #26 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Learning Fully Dense Neural Networks for Image Semantic Segmentation
On the other hand, it learns more efficiently with the more efficient gradient backpropagation.
Ranked #73 on
Semantic Segmentation
on NYU Depth v2
Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information.
Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts
In this paper, we present a joint multi-task learning framework for semantic segmentation and boundary detection.
Weakly-supervised Temporal Action Localization by Uncertainty Modeling
Experimental results show that our uncertainty modeling is effective at alleviating the interference of background frames and brings a large performance gain without bells and whistles.
Weakly Supervised 3D Object Detection from Point Clouds
A crucial task in scene understanding is 3D object detection, which aims to detect and localize the 3D bounding boxes of objects belonging to specific classes.
MonoGRNet: A General Framework for Monocular 3D Object Detection
Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geometric information loss during imagery projection.
Video Instance Segmentation by Instance Flow Assembly
Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes.
Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation
Based on this representation, we introduce a cropping-free temporal fusion approach to model the temporal consistency between video frames.
Reliable Propagation-Correction Modulation for Video Object Segmentation
We introduce two modulators, propagation and correction modulators, to separately perform channel-wise re-calibration on the target frame embeddings according to local temporal correlations and reliable references respectively.
Ranked #3 on
Video Object Segmentation
on DAVIS 2017 (test-dev)
Towards Robust Video Object Segmentation with Adaptive Object Calibration
We consolidate this conditional mask calibration process in a progressive manner, where the object representations and proto-masks evolve to be discriminative iteratively.
Ranked #1 on
Visual Object Tracking
on YouTube-VOS
Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus
Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression.
Ranked #11 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
Referring Expression Segmentation
Referring Video Object Segmentation
+2
Online Video Instance Segmentation via Robust Context Fusion
We propose a robust context fusion network to tackle VIS in an online fashion, which predicts instance segmentation frame-by-frame with a few preceding frames.
Neural Capture of Animatable 3D Human from Monocular Video
We present a novel paradigm of building an animatable 3D human representation from a monocular video input, such that it can be rendered in any unseen poses and views.
Robust Referring Video Object Segmentation with Cyclic Structural Consensus
Our model achieves state-of-the-art performance on R-VOS benchmarks, Ref-DAVIS17 and Ref-Youtube-VOS, and also our RRYTVOS dataset.
Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction
The Multiplane Image (MPI), containing a set of fronto-parallel RGBA layers, is an effective and efficient representation for view synthesis from sparse inputs.
Two-shot Video Object Segmentation
The underlying idea is to generate pseudo labels for unlabeled frames during training and to optimize the model on the combination of labeled and pseudo-labeled data.
High-Fidelity and Freely Controllable Talking Head Video Generation
Our method leverages both self-supervised learned landmarks and 3D face model-based landmarks to model the motion.
PaintSeg: Training-free Segmentation via Painting
The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training.
Rethinking Voice-Face Correlation: A Geometry View
Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion.
Efficient View Synthesis with Neural Radiance Distribution Field
Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF.
Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
We propose a semantic decomposition method based on product quantization, where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics.
$\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the environment where intelligent systems perceive.
