119 research outputs found
Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory
LiDAR Odometry is an essential component in many robotic applications. Unlike
the mainstreamed approaches that focus on improving the accuracy by the
additional inertial sensors, this letter explores the capability of LiDAR-only
odometry through a continuous-time perspective. Firstly, the measurements of
LiDAR are regarded as streaming points continuously captured at high frequency.
Secondly, the LiDAR movement is parameterized by a simple yet effective
continuous-time trajectory. Therefore, our proposed Traj-LO approach tries to
recover the spatial-temporal consistent movement of LiDAR by tightly coupling
the geometric information from LiDAR points and kinematic constraints from
trajectory smoothness. This framework is generalized for different kinds of
LiDAR as well as multi-LiDAR systems. Extensive experiments on the public
datasets demonstrate the robustness and effectiveness of our proposed
LiDAR-only approach, even in scenarios where the kinematic state exceeds the
IMU's measuring range. Our implementation is open-sourced on GitHub.Comment: Video https://youtu.be/hbtKzElYKkQ?si=3KEVy0hlHBsKV8j0 and Project
site https://github.com/kevin2431/Traj-L
FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural Rendering
Despite the promising results of multi-view reconstruction, the recent neural
rendering-based methods, such as implicit surface rendering (IDR) and volume
rendering (NeuS), not only incur a heavy computational burden on training but
also have the difficulties in disentangling the geometric and appearance.
Although having achieved faster training speed than implicit representation and
hash coding, the explicit voxel-based method obtains the inferior results on
recovering surface. To address these challenges, we propose an effective
mesh-based neural rendering approach, named FastMESH, which only samples at the
intersection of ray and mesh. A coarse-to-fine scheme is introduced to
efficiently extract the initial mesh by space carving. More importantly, we
suggest a hexagonal mesh model to preserve surface regularity by constraining
the second-order derivatives of vertices, where only low level of positional
encoding is engaged for neural rendering. The experiments demonstrate that our
approach achieves the state-of-the-art results on both reconstruction and novel
view synthesis. Besides, we obtain 10-fold acceleration on training comparing
to the implicit representation-based methods
Scalable Image Retrieval by Sparse Product Quantization
Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional
feature indexing and retrieval is the crux of large-scale image retrieval. A
recent promising technique is Product Quantization, which attempts to index
high-dimensional image features by decomposing the feature space into a
Cartesian product of low dimensional subspaces and quantizing each of them
separately. Despite the promising results reported, their quantization approach
follows the typical hard assignment of traditional quantization methods, which
may result in large quantization errors and thus inferior search performance.
Unlike the existing approaches, in this paper, we propose a novel approach
called Sparse Product Quantization (SPQ) to encoding the high-dimensional
feature vectors into sparse representation. We optimize the sparse
representations of the feature vectors by minimizing their quantization errors,
making the resulting representation is essentially close to the original data
in practice. Experiments show that the proposed SPQ technique is not only able
to compress data, but also an effective encoding technique. We obtain
state-of-the-art results for ANN search on four public image datasets and the
promising results of content-based image retrieval further validate the
efficacy of our proposed method.Comment: 12 page
End-to-end Weakly-supervised Multiple 3D Hand Mesh Reconstruction from Single Image
In this paper, we consider the challenging task of simultaneously locating
and recovering multiple hands from single 2D image. Previous studies either
focus on single hand reconstruction or solve this problem in a multi-stage way.
Moreover, the conventional two-stage pipeline firstly detects hand areas, and
then estimates 3D hand pose from each cropped patch. To reduce the
computational redundancy in preprocessing and feature extraction, we propose a
concise but efficient single-stage pipeline. Specifically, we design a
multi-head auto-encoder structure for multi-hand reconstruction, where each
head network shares the same feature map and outputs the hand center, pose and
texture, respectively. Besides, we adopt a weakly-supervised scheme to
alleviate the burden of expensive 3D real-world data annotations. To this end,
we propose a series of losses optimized by a stage-wise training scheme, where
a multi-hand dataset with 2D annotations is generated based on the publicly
available single hand datasets. In order to further improve the accuracy of the
weakly supervised model, we adopt several feature consistency constraints in
both single and multiple hand settings. Specifically, the keypoints of each
hand estimated from local features should be consistent with the re-projected
points predicted from global features. Extensive experiments on public
benchmarks including FreiHAND, HO3D, InterHand2.6M and RHD demonstrate that our
method outperforms the state-of-the-art model-based methods in both
weakly-supervised and fully-supervised manners
On the spectrum of operators concerned with the reduced singular Cauchy integral
We investigate spectrums of the reduced singular Cauchy operator and its real and imaginary components
FastHuman: Reconstructing High-Quality Clothed Human in Minutes
We propose an approach for optimizing high-quality clothed human body shapes
in minutes, using multi-view posed images. While traditional neural rendering
methods struggle to disentangle geometry and appearance using only rendering
loss, and are computationally intensive, our method uses a mesh-based patch
warping technique to ensure multi-view photometric consistency, and sphere
harmonics (SH) illumination to refine geometric details efficiently. We employ
oriented point clouds' shape representation and SH shading, which significantly
reduces optimization and rendering times compared to implicit methods. Our
approach has demonstrated promising results on both synthetic and real-world
datasets, making it an effective solution for rapidly generating high-quality
human body shapes. Project page
\href{https://l1346792580123.github.io/nccsfs/}{https://l1346792580123.github.io/nccsfs/}Comment: International Conference on 3D Vision, 3DV 202
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