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Found 15 papers, 11 papers with code
Structured Initialization for Attention in Vision Transformers
By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems.
Self-Supervised Multi-Frame Neural Scene Flow
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.
Fast Kernel Scene Flow
In contrast to current state-of-the-art methods, such as NSFP [25], which employ deep implicit neural functions for modeling scene flow, we present a novel approach that utilizes classical kernel representations.
Convolutional Initialization for Data-Efficient Vision Transformers
Training vision transformer networks on small datasets poses challenges.
Multi-Body Neural Scene Flow
The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art performance.
Robust Point Cloud Processing through Positional Embedding
End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment.
Fast Neural Scene Flow
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points.
Ranked #4 on
Self-supervised Scene Flow Estimation
on Argoverse 2
Trading Positional Complexity vs. Deepness in Coordinate Networks
It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features.
Neural Prior for Trajectory Estimation
Here, we propose a neural trajectory prior to capture continuous spatio-temporal information without the need for offline data.
Neural Scene Flow Prior
A central innovation here is the inclusion of a neural scene flow prior, which uses the architecture of neural networks as a new type of implicit regularizer.
Ranked #2 on
Self-supervised Scene Flow Estimation
on Argoverse 2
PointNetLK Revisited
We address the generalization ability of recent learning-based point cloud registration methods.
One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment
We perform extensive simulation and real-world experiments to validate the efficacy of our approach and compare the performance with state-of-art approaches.
PCRNet: Point Cloud Registration Network using PointNet Encoding
PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion.
MRS-VPR: a multi-resolution sampling based global visual place recognition method
We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching.
A Multi-Domain Feature Learning Method for Visual Place Recognition
Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically.
