Voxel-based methods have achieved state-of-the-art performance for 3D object
detection in autonomous driving. However, their significant computational and
memory costs pose a challenge for their application to resource-constrained
vehicles. One reason for this high resource consumption is the presence of a
large number of redundant background points in Lidar point clouds, resulting in
spatial redundancy in both 3D voxel and dense BEV map representations. To
address this issue, we propose an adaptive inference framework called Ada3D,
which focuses on exploiting the input-level spatial redundancy. Ada3D
adaptively filters the redundant input, guided by a lightweight importance
predictor and the unique properties of the Lidar point cloud. Additionally, we
utilize the BEV features' intrinsic sparsity by introducing the Sparsity
Preserving Batch Normalization. With Ada3D, we achieve 40% reduction for 3D
voxels and decrease the density of 2D BEV feature maps from 100% to 20% without
sacrificing accuracy. Ada3D reduces the model computational and memory cost by
5x, and achieves 1.52x/1.45x end-to-end GPU latency and 1.5x/4.5x GPU peak
memory optimization for the 3D and 2D backbone respectively.

Ada3D 是一种自适应的推断框架，利用轻量级的重要性预测器和点云的独特特性去除冗余输入，减少计算和记忆成本，并通过引入 Sparsity Preserving Batch Normalization 进一步降低了密集特征映射的密度。

Ada3D：自适应推断利用空间冗余以实现高效的三维物体检测

Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for  Efficient 3D Object Detection

Neural surface reconstruction aims to reconstruct accurate 3D surfaces based
on multi-view images. Previous methods based on neural volume rendering mostly
train a fully implicit model with MLPs, which typically require hours of
training for a single scene. Recent efforts explore the explicit volumetric
representation to accelerate the optimization via memorizing significant
information with learnable voxel grids. However, existing voxel-based methods
often struggle in reconstructing fine-grained geometry, even when combined with
an SDF-based volume rendering scheme. We reveal that this is because 1) the
voxel grids tend to break the color-geometry dependency that facilitates
fine-geometry learning, and 2) the under-constrained voxel grids lack spatial
coherence and are vulnerable to local minima. In this work, we present Voxurf,
a voxel-based surface reconstruction approach that is both efficient and
accurate. Voxurf addresses the aforementioned issues via several key designs,
including 1) a two-stage training procedure that attains a coherent coarse
shape and recovers fine details successively, 2) a dual color network that
maintains color-geometry dependency, and 3) a hierarchical geometry feature to
encourage information propagation across voxels. Extensive experiments show
that Voxurf achieves high efficiency and high quality at the same time. On the
DTU benchmark, Voxurf achieves higher reconstruction quality with a 20x
training speedup compared to previous fully implicit methods. Our code is
available at this https URL

本文介绍了一种基于体素的表面重建方法 Voxurf，它通过两步训练、双重颜色网络和层次几何特征等关键设计解决了体素网格在重建精细几何以及缺乏空间一致性方面存在的问题，相较于之前的全隐式方法，Voxurf 在训练速度提高 20 倍的同时还能获得更高的重建质量。

Voxurf: 基于体素的高效精确的神经表面重建

Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction

In recent years, sparse voxel-based methods have become the state-of-the-arts
for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs.
Nevertheless, being oblivious to the underlying geometry, voxel-based methods
suffer from ambiguous features on spatially close objects and struggle with
handling complex and irregular geometries due to the lack of geodesic
information. In view of this, we present Voxel-Mesh Network (VMNet), a novel 3D
deep architecture that operates on the voxel and mesh representations
leveraging both the Euclidean and geodesic information. Intuitively, the
Euclidean information extracted from voxels can offer contextual cues
representing interactions between nearby objects, while the geodesic
information extracted from meshes can help separate objects that are spatially
close but have disconnected surfaces. To incorporate such information from the
two domains, we design an intra-domain attentive module for effective feature
aggregation and an inter-domain attentive module for adaptive feature fusion.
Experimental results validate the effectiveness of VMNet: specifically, on the
challenging ScanNet dataset for large-scale segmentation of indoor scenes, it
outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74.6% vs 72.5%
and 73.6% in mIoU) with a simpler network structure (17M vs 30M and 38M
parameters). Code release: this https URL

本篇论文介绍了一种基于体素和网格表达，利用欧几里得信息和测地信息的深度学习体系结构 VMNet 用于室内场景的 3D 语义分割，并在实验证明了其优越性能，尤其是在大规模的 ScanNet 数据集上。