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Found 28 papers, 7 papers with code
SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations
Optical flow estimation is crucial to a variety of vision tasks.
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation
We propose OCAI, a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between.
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #2 on
Monocular Depth Estimation
on KITTI Eigen split
DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions
Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features.
Neural Mesh Fusion: Unsupervised 3D Planar Surface Understanding
This paper presents Neural Mesh Fusion (NMF), an efficient approach for joint optimization of polygon mesh from multi-view image observations and unsupervised 3D planar-surface parsing of the scene.
HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation
Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task.
Efficient option pricing with unary-based photonic computing chip and generative adversarial learning
In the modern financial industry system, the structure of products has become more and more complex, and the bottleneck constraint of classical computing power has already restricted the development of the financial industry.
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #12 on
Monocular Depth Estimation
on KITTI Eigen split
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e. g., drivable area, in autonomous driving.
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.
Ranked #5 on
Zero-Shot Transfer 3D Point Cloud Classification
on ModelNet40
(using extra training data)
Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation
Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene.
EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation
the number of views and frames.
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation.
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling
We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames.
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction
Next, we use a conditional regenerator, which takes the redacted image and the dense predictions as inputs, and reconstructs the original image by filling in the missing structural information.
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion.
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Generalizable 3D part segmentation is important but challenging in vision and robotics.
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e. g., drivable area, in autonomous driving.
Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild
While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations.
Learning Implicit Feature Alignment Function for Semantic Segmentation
As such, IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation
A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone.
A photonic chip-based machine learning approach for the prediction of molecular properties
Machine learning methods have revolutionized the discovery process of new molecules and materials.
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device
Such a deployment scheme best utilizes the available processing power on the smartphone and enables real-time operation of our adaptive video segmentation algorithm.
HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation
While deeply supervised networks are common in recent literature, they typically impose the same learning objective on all transitional layers despite their varying representation powers.
Ranked #4 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Perceptual Consistency in Video Segmentation
More specifically, by measuring the perceptual consistency between the predicted segmentation and the available ground truth on a nearby frame and combining it with the segmentation confidence, we can accurately assess the classification correctness on each pixel.
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation
Since inconsistency mainly arises from the model's uncertainty in its output, we propose an adaptation scheme where the model learns from its own segmentation decisions as it streams a video, which allows producing more confident and temporally consistent labeling for similarly-looking pixels across frames.
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
In order to enable such knowledge distillation across two different visual tasks, we introduce a small, trainable network that translates the predicted depth map to a semantic segmentation map, which can then be supervised by the teacher network.
PieAPP: Perceptual Image-Error Assessment through Pairwise Preference
Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels.
Ranked #1 on
Video Quality Assessment
on MSU SR-QA Dataset
