Vision-based perception for autonomous driving requires an explicit modeling
of a 3D space, where 2D latent representations are mapped and subsequent 3D
operators are applied. However, operating on dense latent spaces introduces a
cubic time and space complexity, which limits scalability in terms of
perception range or spatial resolution. Existing approaches compress the dense
representation using projections like Bird's Eye View (BEV) or Tri-Perspective
View (TPV). Although efficient, these projections result in information loss,
especially for tasks like semantic occupancy prediction. To address this, we
propose SparseOcc, an efficient occupancy network inspired by sparse point
cloud processing. It utilizes a lossless sparse latent representation with
three key innovations. Firstly, a 3D sparse diffuser performs latent completion
using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature
pyramid and sparse interpolation enhance scales with information from others.
Finally, the transformer head is redesigned as a sparse variant. SparseOcc
achieves a remarkable 74.9% reduction on FLOPs over the dense baseline.
Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in
part can be attributed to the sparse representation's ability to avoid
hallucinations on empty voxels.

提出了 SparseOcc，一种受稀疏点云处理启发的高效占据网络，利用了无损稀疏潜在表示的三个关键创新。通过空间分解的 3D 稀疏卷积核执行潜在补全的 3D 稀疏扩散器；通过特征金字塔和稀疏插值从其他尺度获取信息；将 Transformer 头改造为稀疏变种。SparseOcc 在 FLOP 上实现了惊人的 74.9% 减少，同时在精确度上有所提高。

SparseOcc（稀疏隐含表示）：重新考虑基于视觉的语义占用预测

SparseOcc: Rethinking Sparse Latent Representation for Vision-Based  Semantic Occupancy Prediction

3D hand pose estimation from images has seen considerable interest from the
literature, with new methods improving overall 3D accuracy. One current
challenge is to address hand-to-hand interaction where self-occlusions and
finger articulation pose a significant problem to estimation. Little work has
applied physical constraints that minimize the hand intersections that occur as
a result of noisy estimation. This work addresses the intersection of hands by
exploiting an occupancy network that represents the hand's volume as a
continuous manifold. This allows us to model the probability distribution of
points being inside a hand. We designed an intersection loss function to
minimize the likelihood of hand-to-point intersections. Moreover, we propose a
new hand mesh parameterization that is superior to the commonly used MANO model
in many respects including lower mesh complexity, underlying 3D skeleton
extraction, watertightness, etc. On the benchmark InterHand2.6M dataset, the
models trained using our intersection loss achieve better results than the
state-of-the-art by significantly decreasing the number of hand intersections
while lowering the mean per-joint positional error. Additionally, we
demonstrate superior performance for 3D hand uplift on Re:InterHand and SMILE
datasets and show reduced hand-to-hand intersections for complex domains such
as sign-language pose estimation.

利用占用网络解决手与手交互带来的干扰问题，降低手的相交数，并提出了一种新的手网格参数化方法，实现更好的性能和更低的误差。