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Found 19 papers, 6 papers with code
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
The model is trained on a joint dataset of video depth and unlabeled images, similar to Depth Anything V2.
Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer
In this work, we introduce Direct3D, a native 3D generative model scalable to in-the-wild input images, without requiring a multiview diffusion model or SDS optimization.
Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention
Specifically, these methods assume that the input images should comply with a predefined camera type, e. g. a perspective camera with a fixed focal length, leading to distorted shapes when the assumption fails.
S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation
Autonomous driving simulation system plays a crucial role in enhancing self-driving data and simulating complex and rare traffic scenarios, ensuring navigation safety.
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice.
Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration with Correspondences
Moreover, our method can be adapted to address the challenging problem of simultaneous pose and registration.
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
We verify the effectiveness of our NeRF-LiDAR by training different 3D segmentation models on the generated LiDAR point clouds.
Single-view Neural Radiance Fields with Depth Teacher
Moreover, it has poor generalizations to new scenes and requires retraining or fine-tuning on each scene.
S-NeRF: Neural Radiance Fields for Street Views
Specifically, we improve the scene parameterization function and the camera poses for learning better neural representations from street views.
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
Semantic segmentation models struggle to generalize in the presence of domain shift.
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning
We present an approach to contrastive representation learning for semantic segmentation.
Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation
Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
Ranked #5 on
Optical Flow Estimation
on KITTI 2015 (train)
Domain-invariant Stereo Matching Networks
State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast, and texture.
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities.
Ranked #4 on
Stereo Depth Estimation
on Spring
Hypergraph Convolution and Hypergraph Attention
To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i. e., hypergraph convolution and hypergraph attention.
Augmented LiDAR Simulator for Autonomous Driving
Instead, we can simply deploy a vehicle with a LiDAR scanner to sweep the street of interests to obtain the background point cloud, based on which annotated point cloud can be automatically generated.
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks
In this paper, we develop a meta-level NN (MLNN) model that learns meta-knowledge on data-specific properties of images during learning and that dynamically adapts its weights during application according to the properties of the images input.
Fully Connected Guided Image Filtering
This paper presents a linear time fully connected guided filter by introducing the minimum spanning tree (MST) to the guided filter (GF).
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing
In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel, which enables the filter to smooth out high-contrast details and textures while preserving major image structures very well.
