Text-to-3D generation aims to create 3D assets from text-to-image diffusion
models. However, existing methods face an inherent bottleneck in generation
quality because the widely-used objectives such as Score Distillation Sampling
(SDS) inappropriately omit U-Net jacobians for swift generation, leading to
significant bias compared to the "true" gradient obtained by full denoising
sampling. This bias brings inconsistent updating direction, resulting in
implausible 3D generation e.g., color deviation, Janus problem, and
semantically inconsistent details). In this work, we propose Pose-dependent
Consistency Distillation Sampling (PCDS), a novel yet efficient objective for
diffusion-based 3D generation tasks. Specifically, PCDS builds the
pose-dependent consistency function within diffusion trajectories, allowing to
approximate true gradients through minimal sampling steps (1-3). Compared to
SDS, PCDS can acquire a more accurate updating direction with the same sampling
time (1 sampling step), while enabling few-step (2-3) sampling to trade compute
for higher generation quality. For efficient generation, we propose a
coarse-to-fine optimization strategy, which first utilizes 1-step PCDS to
create the basic structure of 3D objects, and then gradually increases PCDS
steps to generate fine-grained details. Extensive experiments demonstrate that
our approach outperforms the state-of-the-art in generation quality and
training efficiency, conspicuously alleviating the implausible 3D generation
issues caused by the deviated updating direction. Moreover, it can be simply
applied to many 3D generative applications to yield impressive 3D assets,
please see our project page: this https URL

提出了一种基于姿态相关性蒸馏采样（PCDS）的扩散式三维生成任务的新目标，其通过最小采样步骤（1-3）建立了在扩散轨迹内的姿态相关性函数，以近似真实梯度，并通过粗到细的优化策略实现高质量的三维物体生成。

VividDreamer：朝向高保真高效的文本到 3D 生成

VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation

Light-weight time-of-flight (ToF) depth sensors are compact and
cost-efficient, and thus widely used on mobile devices for tasks such as
autofocus and obstacle detection. However, due to the sparse and noisy depth
measurements, these sensors have rarely been considered for dense geometry
reconstruction. In this work, we present the first dense SLAM system with a
monocular camera and a light-weight ToF sensor. Specifically, we propose a
multi-modal implicit scene representation that supports rendering both the
signals from the RGB camera and light-weight ToF sensor which drives the
optimization by comparing with the raw sensor inputs. Moreover, in order to
guarantee successful pose tracking and reconstruction, we exploit a predicted
depth as an intermediate supervision and develop a coarse-to-fine optimization
strategy for efficient learning of the implicit representation. At last, the
temporal information is explicitly exploited to deal with the noisy signals
from light-weight ToF sensors to improve the accuracy and robustness of the
system. Experiments demonstrate that our system well exploits the signals of
light-weight ToF sensors and achieves competitive results both on camera
tracking and dense scene reconstruction. Project page:
https://zju3dv.github.io/tof_slam/.

通过使用轻量级 ToF 传感器的信号，结合多模式隐式场景表示、粗糙到精细的优化策略和时间信息处理，该研究提出了一种用于相机追踪和稠密场景重建的稀疏光准测距技术，取得了良好的结果。